{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "04c15a28",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-24T02:58:29.339347Z",
     "iopub.status.busy": "2023-05-24T02:58:29.338665Z",
     "iopub.status.idle": "2023-05-24T02:58:44.070120Z",
     "shell.execute_reply": "2023-05-24T02:58:44.069173Z"
    },
    "executionInfo": {
     "elapsed": 2844,
     "status": "ok",
     "timestamp": 1684409199330,
     "user": {
      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
     },
     "user_tz": -480
    },
    "id": "wHsFZo6akD8M",
    "outputId": "d017ea14-1ff6-429f-d7da-2f6ad9e82ec1",
    "papermill": {
     "duration": 14.742964,
     "end_time": "2023-05-24T02:58:44.072505",
     "exception": false,
     "start_time": "2023-05-24T02:58:29.329541",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
    "batch_size = 8\n",
    "\n",
    "device=\"cuda\"\n",
    "\n",
    "#uncomment when using colab or local cpu\n",
    "# train_dataset = torchvision.datasets.cifar.CIFAR100(root='cifar100', train=True, transform=transform, download=True)\n",
    "# test_dataset = torchvision.datasets.cifar.CIFAR100(root='cifar100', train=False, transform=transform, download=True)\n",
    "# train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=2)\n",
    "# test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=2)\n",
    "\n",
    "#uncomment when using kaggle\n",
    "\n",
    "train_dataset = torchvision.datasets.ImageFolder(\"/kaggle/input/cifar100/CIFAR100/TRAIN\", transform=transform)\n",
    "\n",
    "test_dataset = torchvision.datasets.ImageFolder(\"/kaggle/input/cifar100/CIFAR100/TEST\", transform=transform)\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=2)\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "31b8d25a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-24T02:58:44.085107Z",
     "iopub.status.busy": "2023-05-24T02:58:44.084619Z",
     "iopub.status.idle": "2023-05-24T02:58:44.494729Z",
     "shell.execute_reply": "2023-05-24T02:58:44.493630Z"
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1684409199330,
     "user": {
      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
     },
     "user_tz": -480
    },
    "id": "VjkCK8NykPMb",
    "outputId": "d8b6db1d-275c-4979-f5c6-411c763dea3f",
    "papermill": {
     "duration": 0.420506,
     "end_time": "2023-05-24T02:58:44.498889",
     "exception": false,
     "start_time": "2023-05-24T02:58:44.078383",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([8])\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# functions to show an image\n",
    "\n",
    "\n",
    "def imshow(img):\n",
    "    img = img / 2 + 0.5     # unnormalize\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# get some random training images\n",
    "dataiter = iter(train_loader)\n",
    "# images, labels = dataiter.next()\n",
    "images, labels = next(dataiter)\n",
    "\n",
    "# show images\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "# print labels\n",
    "print(labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "612b193e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-24T02:58:44.513917Z",
     "iopub.status.busy": "2023-05-24T02:58:44.513614Z",
     "iopub.status.idle": "2023-05-24T02:58:44.550694Z",
     "shell.execute_reply": "2023-05-24T02:58:44.549852Z"
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1684409199331,
     "user": {
      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
     },
     "user_tz": -480
    },
    "id": "1mY2iwmCkcpA",
    "papermill": {
     "duration": 0.047449,
     "end_time": "2023-05-24T02:58:44.552884",
     "exception": false,
     "start_time": "2023-05-24T02:58:44.505435",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.nn import init\n",
    "\n",
    "def conv3x3(in_planes, out_planes, stride=1):\n",
    "    \"3x3 convolution with padding\"\n",
    "    return nn.Conv2d(\n",
    "        in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False\n",
    "    )\n",
    "\n",
    "\n",
    "class BasicBlock(nn.Module):\n",
    "    expansion = 1\n",
    "\n",
    "    def __init__(\n",
    "        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False\n",
    "    ):\n",
    "        super(BasicBlock, self).__init__()\n",
    "        self.conv1 = conv3x3(inplanes, planes, stride)\n",
    "        self.bn1 = nn.BatchNorm2d(planes)\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "        self.conv2 = conv3x3(planes, planes)\n",
    "        self.bn2 = nn.BatchNorm2d(planes)\n",
    "        self.downsample = downsample\n",
    "        self.stride = stride\n",
    "\n",
    "        if use_triplet_attention:\n",
    "            self.triplet_attention = TripletAttention(planes, 16)\n",
    "        else:\n",
    "            self.triplet_attention = None\n",
    "\n",
    "    def forward(self, x):\n",
    "        residual = x\n",
    "\n",
    "        out = self.conv1(x)\n",
    "        out = self.bn1(out)\n",
    "        out = self.relu(out)\n",
    "\n",
    "        out = self.conv2(out)\n",
    "        out = self.bn2(out)\n",
    "\n",
    "        if self.downsample is not None:\n",
    "            residual = self.downsample(x)\n",
    "\n",
    "        if not self.triplet_attention is None:\n",
    "            out = self.triplet_attention(out)\n",
    "\n",
    "        out += residual\n",
    "        out = self.relu(out)\n",
    "\n",
    "        return out\n",
    "\n",
    "\n",
    "class Bottleneck(nn.Module):\n",
    "    expansion = 4\n",
    "\n",
    "    def __init__(\n",
    "        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False\n",
    "    ):\n",
    "        super(Bottleneck, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n",
    "        self.bn1 = nn.BatchNorm2d(planes)\n",
    "        self.conv2 = nn.Conv2d(\n",
    "            planes, planes, kernel_size=3, stride=stride, padding=1, bias=False\n",
    "        )\n",
    "        self.bn2 = nn.BatchNorm2d(planes)\n",
    "        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n",
    "        self.bn3 = nn.BatchNorm2d(planes * 4)\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "        self.downsample = downsample\n",
    "        self.stride = stride\n",
    "\n",
    "        if use_triplet_attention:\n",
    "            self.triplet_attention = TripletAttention(planes * 4, 16)\n",
    "        else:\n",
    "            self.triplet_attention = None\n",
    "\n",
    "    def forward(self, x):\n",
    "        residual = x\n",
    "\n",
    "        out = self.conv1(x)\n",
    "        out = self.bn1(out)\n",
    "        out = self.relu(out)\n",
    "\n",
    "        out = self.conv2(out)\n",
    "        out = self.bn2(out)\n",
    "        out = self.relu(out)\n",
    "\n",
    "        out = self.conv3(out)\n",
    "        out = self.bn3(out)\n",
    "\n",
    "        if self.downsample is not None:\n",
    "            residual = self.downsample(x)\n",
    "\n",
    "        if not self.triplet_attention is None:\n",
    "            out = self.triplet_attention(out)\n",
    "\n",
    "        out += residual\n",
    "        out = self.relu(out)\n",
    "\n",
    "        return out\n",
    "\n",
    "\n",
    "class ResNet(nn.Module):\n",
    "    def __init__(self, block, layers, network_type, num_classes, att_type=None):\n",
    "        self.inplanes = 64\n",
    "        super(ResNet, self).__init__()\n",
    "        self.network_type = network_type\n",
    "        # different model config between ImageNet and CIFAR\n",
    "        if network_type == \"ImageNet\":\n",
    "            self.conv1 = nn.Conv2d(\n",
    "                3, 64, kernel_size=7, stride=2, padding=3, bias=False\n",
    "            )\n",
    "            self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
    "            self.avgpool = nn.AvgPool2d(7)\n",
    "        else:\n",
    "            self.conv1 = nn.Conv2d(\n",
    "                3, 64, kernel_size=3, stride=1, padding=1, bias=False\n",
    "            )\n",
    "\n",
    "        self.bn1 = nn.BatchNorm2d(64)\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "\n",
    "        self.layer1 = self._make_layer(block, 64, layers[0], att_type=att_type)\n",
    "        self.layer2 = self._make_layer(\n",
    "            block, 128, layers[1], stride=2, att_type=att_type\n",
    "        )\n",
    "        self.layer3 = self._make_layer(\n",
    "            block, 256, layers[2], stride=2, att_type=att_type\n",
    "        )\n",
    "        self.layer4 = self._make_layer(\n",
    "            block, 512, layers[3], stride=2, att_type=att_type\n",
    "        )\n",
    "\n",
    "        self.fc = nn.Linear(512 * block.expansion, num_classes)\n",
    "\n",
    "        init.kaiming_normal_(self.fc.weight)\n",
    "        for key in self.state_dict():\n",
    "            if key.split(\".\")[-1] == \"weight\":\n",
    "                if \"conv\" in key:\n",
    "                    init.kaiming_normal_(self.state_dict()[key], mode=\"fan_out\")\n",
    "                if \"bn\" in key:\n",
    "                    if \"SpatialGate\" in key:\n",
    "                        self.state_dict()[key][...] = 0\n",
    "                    else:\n",
    "                        self.state_dict()[key][...] = 1\n",
    "            elif key.split(\".\")[-1] == \"bias\":\n",
    "                self.state_dict()[key][...] = 0\n",
    "\n",
    "    def _make_layer(self, block, planes, blocks, stride=1, att_type=None):\n",
    "        downsample = None\n",
    "        if stride != 1 or self.inplanes != planes * block.expansion:\n",
    "            downsample = nn.Sequential(\n",
    "                nn.Conv2d(\n",
    "                    self.inplanes,\n",
    "                    planes * block.expansion,\n",
    "                    kernel_size=1,\n",
    "                    stride=stride,\n",
    "                    bias=False,\n",
    "                ),\n",
    "                nn.BatchNorm2d(planes * block.expansion),\n",
    "            )\n",
    "\n",
    "        layers = []\n",
    "        layers.append(\n",
    "            block(\n",
    "                self.inplanes,\n",
    "                planes,\n",
    "                stride,\n",
    "                downsample,\n",
    "                use_triplet_attention=att_type == \"TripletAttention\",\n",
    "            )\n",
    "        )\n",
    "        self.inplanes = planes * block.expansion\n",
    "        for i in range(1, blocks):\n",
    "            layers.append(\n",
    "                block(\n",
    "                    self.inplanes,\n",
    "                    planes,\n",
    "                    use_triplet_attention=att_type == \"TripletAttention\",\n",
    "                )\n",
    "            )\n",
    "\n",
    "        return nn.Sequential(*layers)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.bn1(x)\n",
    "        x = self.relu(x)\n",
    "        if self.network_type == \"ImageNet\":\n",
    "            x = self.maxpool(x)\n",
    "\n",
    "        x = self.layer1(x)\n",
    "        x = self.layer2(x)\n",
    "        x = self.layer3(x)\n",
    "        x = self.layer4(x)\n",
    "\n",
    "        if self.network_type == \"ImageNet\":\n",
    "            x = self.avgpool(x)\n",
    "        else:\n",
    "            x = F.avg_pool2d(x, 4)\n",
    "        x = x.view(x.size(0), -1)\n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d683b243",
   "metadata": {
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     "duration": 0.006019,
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     "start_time": "2023-05-24T02:58:44.559332",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### channel attention trial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "21251f11",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "class ChannelAttention(nn.Module):\n",
    "    def __init__(self, in_channels, reduction_ratio=16):\n",
    "        super(ChannelAttention, self).__init__()\n",
    "        \n",
    "        self.avg_pool = nn.AdaptiveAvgPool2d(1)\n",
    "        self.fc = nn.Sequential(\n",
    "            nn.Linear(in_channels, in_channels // reduction_ratio),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Linear(in_channels // reduction_ratio, in_channels),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # 输入x的尺寸：[batch_size, in_channels, height, width]\n",
    "        \n",
    "        # 全局平均池化，输出尺寸：[batch_size, in_channels, 1, 1]\n",
    "        feat = self.avg_pool(x)\n",
    "        \n",
    "        # 压缩通道维度，输出尺寸：[batch_size, in_channels // reduction_ratio, 1, 1]\n",
    "        feat = feat.view(feat.size(0), -1)\n",
    "        \n",
    "        # 全连接层，输出尺寸：[batch_size, in_channels]\n",
    "        feat = self.fc(feat)\n",
    "        \n",
    "        # 将通道权重应用到输入特征图上，输出尺寸：[batch_size, in_channels, height, width]\n",
    "        feat = torch.unsqueeze(feat, 2)\n",
    "        feat = torch.unsqueeze(feat, 3)\n",
    "        feat = x * feat\n",
    "        \n",
    "        return feat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c05f6c9f",
   "metadata": {
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     "duration": 0.006109,
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     "start_time": "2023-05-24T02:58:44.596622",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 定义SKNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "32d77c5b",
   "metadata": {
    "execution": {
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   "source": [
    "\n",
    "#sknet\n",
    "class SKConv(nn.Module):\n",
    "    def __init__(self, features, M=2, G=32, r=16, stride=1 ,L=32):\n",
    "        \"\"\" Constructor\n",
    "        Args:\n",
    "            features: input channel dimensionality.\n",
    "            M: the number of branchs.\n",
    "            G: num of convolution groups.\n",
    "            r: the ratio for compute d, the length of z.\n",
    "            stride: stride, default 1.\n",
    "            L: the minimum dim of the vector z in paper, default 32.\n",
    "        \"\"\"\n",
    "        super(SKConv, self).__init__()\n",
    "        d = max(int(features/r), L)\n",
    "        self.M = M\n",
    "        self.features = features\n",
    "        self.convs = nn.ModuleList([])\n",
    "        for i in range(M):\n",
    "            self.convs.append(nn.Sequential(\n",
    "                nn.Conv2d(features, features, kernel_size=3, stride=stride, padding=1+i, dilation=1+i, groups=G, bias=False),\n",
    "                nn.BatchNorm2d(features),\n",
    "                nn.ReLU(inplace=True)\n",
    "            ))\n",
    "        self.gap = nn.AdaptiveAvgPool2d((1,1))\n",
    "        self.fc = nn.Sequential(nn.Conv2d(features, d, kernel_size=1, stride=1, bias=False),\n",
    "                                nn.BatchNorm2d(d),\n",
    "                                nn.ReLU(inplace=True))\n",
    "        self.fcs = nn.ModuleList([])\n",
    "        for i in range(M):\n",
    "            self.fcs.append(\n",
    "                 nn.Conv2d(d, features, kernel_size=1, stride=1)\n",
    "            )\n",
    "        self.softmax = nn.Softmax(dim=1)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        \n",
    "        batch_size = x.shape[0]\n",
    "        \n",
    "        feats = [conv(x) for conv in self.convs]      \n",
    "        feats = torch.cat(feats, dim=1)\n",
    "        feats = feats.view(batch_size, self.M, self.features, feats.shape[2], feats.shape[3])\n",
    "        \n",
    "        feats_U = torch.sum(feats, dim=1)\n",
    "        feats_S = self.gap(feats_U)\n",
    "        feats_Z = self.fc(feats_S)\n",
    "\n",
    "        attention_vectors = [fc(feats_Z) for fc in self.fcs]\n",
    "        attention_vectors = torch.cat(attention_vectors, dim=1)\n",
    "        attention_vectors = attention_vectors.view(batch_size, self.M, self.features, 1, 1)\n",
    "        attention_vectors = self.softmax(attention_vectors)\n",
    "        \n",
    "        feats_V = torch.sum(feats*attention_vectors, dim=1)\n",
    "        \n",
    "        return feats_V\n",
    "\n",
    "\n",
    "class SKUnit(nn.Module):\n",
    "    def __init__(self, in_features, mid_features, out_features, M=2, G=32, r=16, stride=1, L=32):\n",
    "        \"\"\" Constructor\n",
    "        Args:\n",
    "            in_features: input channel dimensionality.\n",
    "            out_features: output channel dimensionality.\n",
    "            M: the number of branchs.\n",
    "            G: num of convolution groups.\n",
    "            r: the ratio for compute d, the length of z.\n",
    "            mid_features: the channle dim of the middle conv with stride not 1, default out_features/2.\n",
    "            stride: stride.\n",
    "            L: the minimum dim of the vector z in paper.\n",
    "        \"\"\"\n",
    "        super(SKUnit, self).__init__()\n",
    "        \n",
    "        self.conv1 = nn.Sequential(\n",
    "            nn.Conv2d(in_features, mid_features, 1, stride=1, bias=False),\n",
    "            nn.BatchNorm2d(mid_features),\n",
    "            nn.ReLU(inplace=True)\n",
    "            )\n",
    "        \n",
    "        self.conv2_sk = SKConv(mid_features, M=M, G=G, r=r, stride=stride, L=L)\n",
    "        \n",
    "        self.conv3 = nn.Sequential(\n",
    "            nn.Conv2d(mid_features, out_features, 1, stride=1, bias=False),\n",
    "            nn.BatchNorm2d(out_features)\n",
    "            )\n",
    "        \n",
    "\n",
    "        if in_features == out_features: # when dim not change, input_features could be added diectly to out\n",
    "            self.shortcut = nn.Sequential()\n",
    "        else: # when dim not change, input_features should also change dim to be added to out\n",
    "            self.shortcut = nn.Sequential(\n",
    "                nn.Conv2d(in_features, out_features, 1, stride=stride, bias=False),\n",
    "                nn.BatchNorm2d(out_features)\n",
    "            )\n",
    "        \n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        residual = x\n",
    "        \n",
    "        out = self.conv1(x)\n",
    "        out = self.conv2_sk(out)\n",
    "        out = self.conv3(out)\n",
    "        \n",
    "        return self.relu(out + self.shortcut(residual))\n",
    "\n",
    "class SKNet(nn.Module):\n",
    "    def __init__(self, class_num, nums_block_list = [3, 4, 6, 3], strides_list = [1, 2, 2, 2]):\n",
    "        super(SKNet, self).__init__()\n",
    "        self.basic_conv = nn.Sequential(\n",
    "            nn.Conv2d(3, 64, 7, 2, 3, bias=False),\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.ReLU(inplace=True),\n",
    "        )\n",
    "        \n",
    "        self.maxpool = nn.MaxPool2d(3,2,1)\n",
    "        \n",
    "        self.stage_1 = self._make_layer(64, 64, 64, nums_block=nums_block_list[0], stride=strides_list[0])\n",
    "        self.ca_1 = ChannelAttention(64)\n",
    "        self.stage_2 = self._make_layer(64, 128, 128, nums_block=nums_block_list[1], stride=strides_list[1])\n",
    "        self.ca_2 = ChannelAttention(128)\n",
    "        self.stage_3 = self._make_layer(128, 256, 256, nums_block=nums_block_list[2], stride=strides_list[2])\n",
    "        self.ca_3 = ChannelAttention(256)\n",
    "        self.stage_4 = self._make_layer(256, 512, 512, nums_block=nums_block_list[3], stride=strides_list[3])\n",
    "     \n",
    "        self.gap = nn.AdaptiveAvgPool2d((1, 1))\n",
    "        self.classifier = nn.Linear(512, class_num)\n",
    "        \n",
    "    def _make_layer(self, in_feats, mid_feats, out_feats, nums_block, stride=1):\n",
    "        layers=[SKUnit(in_feats, mid_feats, out_feats, stride=stride)]\n",
    "        for _ in range(1,nums_block):\n",
    "            layers.append(SKUnit(out_feats, mid_feats, out_feats))\n",
    "        return nn.Sequential(*layers)\n",
    "\n",
    "    def forward(self, x):\n",
    "        fea = self.basic_conv(x)\n",
    "        fea = self.maxpool(fea)\n",
    "        fea = self.stage_1(fea)\n",
    "        fea = self.stage_2(fea)\n",
    "        fea = self.stage_3(fea)\n",
    "        fea = self.stage_4(fea)\n",
    "        fea = self.gap(fea)\n",
    "        fea = torch.squeeze(fea)\n",
    "        fea = self.classifier(fea)\n",
    "        return fea\n",
    "\n",
    "def SKNet26(nums_class=100):\n",
    "    return SKNet(nums_class, [2, 2, 2, 2])\n",
    "def SKNet50(nums_class=100):\n",
    "    return SKNet(nums_class, [3, 4, 6, 3])\n",
    "def SKNet101(nums_class=100):\n",
    "    return SKNet(nums_class, [3, 4, 23, 3])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7a44c206",
   "metadata": {
    "execution": {
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    },
    "executionInfo": {
     "elapsed": 680,
     "status": "ok",
     "timestamp": 1684414637992,
     "user": {
      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
     },
     "user_tz": -480
    },
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   "outputs": [],
   "source": [
    "\n",
    "net=SKNet26().to(device)\n",
    "#net=ResidualNet(\"CIFAR100\",50,100,None).to(device)\n",
    "#print(net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cabb8270",
   "metadata": {
    "execution": {
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    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1684414638807,
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      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
     },
     "user_tz": -480
    },
    "id": "35VI01P_kqal",
    "outputId": "64747940-f10a-4982-df2f-d5bc5a500b78",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000\n"
     ]
    }
   ],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=0.0005)\n",
    "print(len(train_loader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ec55e19",
   "metadata": {
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
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     "status": "completed"
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    "tags": []
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ea40ac5d",
   "metadata": {
    "execution": {
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    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1684414638808,
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      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
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     "user_tz": -480
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   "source": [
    "from tqdm import tqdm\n",
    "def train(epoch):\n",
    "    net.train()\n",
    "    # Loop over each batch from the training set\n",
    "    train_tqdm = tqdm(train_loader, desc=\"Epoch \" + str(epoch))\n",
    "    for batch_idx, (data, target) in enumerate(train_tqdm):\n",
    "        # Copy data to GPU if needed\n",
    "        data = data.to(device)\n",
    "        target = target.to(device)\n",
    "        # Zero gradient buffers\n",
    "        optimizer.zero_grad()\n",
    "        # Pass data through the network\n",
    "        output = net(data)\n",
    "        # Calculate loss\n",
    "        loss = criterion(output, target)\n",
    "        # Backpropagate\n",
    "        loss.backward()\n",
    "        # Update weights\n",
    "        optimizer.step()  # w - alpha * dL / dw\n",
    "        train_tqdm.set_postfix({\"loss\": \"%.3g\" % loss.item()})\n",
    "\n",
    "def validate(lossv,top1AccuracyList,top5AccuracyList):\n",
    "    net.eval()\n",
    "    val_loss = 0\n",
    "    top1Correct = 0\n",
    "    top5Correct = 0\n",
    "    for index,(data, target) in enumerate(test_loader):\n",
    "        data = data.to(device)\n",
    "        labels = target.to(device)\n",
    "        outputs = net(data)\n",
    "        val_loss += criterion(outputs, labels).data.item()\n",
    "        _, top1Predicted = torch.max(outputs.data, 1)\n",
    "        top5Predicted = torch.topk(outputs.data, k=5, dim=1, largest=True)[1]\n",
    "        top1Correct += (top1Predicted == labels).cpu().sum().item()\n",
    "        label_resize = labels.view(-1, 1).expand_as(top5Predicted)\n",
    "        top5Correct += torch.eq(top5Predicted, label_resize).view(-1).cpu().sum().float().item()\n",
    "\n",
    "    val_loss /= len(test_loader)\n",
    "    lossv.append(val_loss)\n",
    "\n",
    "    top1Acc=100*top1Correct / len(test_loader.dataset)\n",
    "    top5Acc=100*top5Correct / len(test_loader.dataset)\n",
    "    top1AccuracyList.append(top1Acc)\n",
    "    top5AccuracyList.append(top5Acc)\n",
    "    \n",
    "    print('\\nValidation set: Average loss: {:.4f}, Top1Accuracy: {}/{} ({:.0f}%) Top5Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
    "        val_loss, top1Correct, len(test_loader.dataset), top1Acc,top5Correct, len(test_loader.dataset), top5Acc))\n",
    "#     accuracy = 100. * correct.to(torch.float32) / len(testloader.dataset)\n",
    "#     accv.append(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "311ab418",
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 0: 100%|██████████| 5000/5000 [03:20<00:00, 24.98it/s, loss=4.07]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 3.8170, Top1Accuracy: 1282/10000 (13%) Top5Accuracy: 3623.0/10000 (36%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1: 100%|██████████| 5000/5000 [03:40<00:00, 22.71it/s, loss=4.09]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 3.3943, Top1Accuracy: 1836/10000 (18%) Top5Accuracy: 4550.0/10000 (46%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 2: 100%|██████████| 5000/5000 [03:39<00:00, 22.79it/s, loss=3.14]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 3.1008, Top1Accuracy: 2461/10000 (25%) Top5Accuracy: 5442.0/10000 (54%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 3: 100%|██████████| 5000/5000 [03:16<00:00, 25.48it/s, loss=3.12]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.8810, Top1Accuracy: 2821/10000 (28%) Top5Accuracy: 5871.0/10000 (59%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 4: 100%|██████████| 5000/5000 [03:38<00:00, 22.90it/s, loss=2.66]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.7467, Top1Accuracy: 3103/10000 (31%) Top5Accuracy: 6180.0/10000 (62%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 5: 100%|██████████| 5000/5000 [03:26<00:00, 24.18it/s, loss=2.26]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.7008, Top1Accuracy: 3323/10000 (33%) Top5Accuracy: 6426.0/10000 (64%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 6: 100%|██████████| 5000/5000 [03:12<00:00, 26.00it/s, loss=2.42]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.6177, Top1Accuracy: 3471/10000 (35%) Top5Accuracy: 6538.0/10000 (65%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 7: 100%|██████████| 5000/5000 [03:12<00:00, 26.04it/s, loss=2.32]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.5190, Top1Accuracy: 3688/10000 (37%) Top5Accuracy: 6748.0/10000 (67%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 8: 100%|██████████| 5000/5000 [03:10<00:00, 26.31it/s, loss=3.25]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.5499, Top1Accuracy: 3683/10000 (37%) Top5Accuracy: 6684.0/10000 (67%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 9: 100%|██████████| 5000/5000 [03:07<00:00, 26.70it/s, loss=2.37]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.5520, Top1Accuracy: 3707/10000 (37%) Top5Accuracy: 6729.0/10000 (67%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 10: 100%|██████████| 5000/5000 [03:10<00:00, 26.19it/s, loss=2.52]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.6722, Top1Accuracy: 3735/10000 (37%) Top5Accuracy: 6763.0/10000 (68%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 11: 100%|██████████| 5000/5000 [03:43<00:00, 22.39it/s, loss=2.73]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.4240, Top1Accuracy: 3976/10000 (40%) Top5Accuracy: 6933.0/10000 (69%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 12: 100%|██████████| 5000/5000 [03:52<00:00, 21.47it/s, loss=1.24]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.4184, Top1Accuracy: 3939/10000 (39%) Top5Accuracy: 6914.0/10000 (69%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 13: 100%|██████████| 5000/5000 [03:24<00:00, 24.50it/s, loss=1.66]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.4812, Top1Accuracy: 4021/10000 (40%) Top5Accuracy: 6995.0/10000 (70%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 14: 100%|██████████| 5000/5000 [03:36<00:00, 23.09it/s, loss=1.54]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.4519, Top1Accuracy: 3962/10000 (40%) Top5Accuracy: 6982.0/10000 (70%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 15: 100%|██████████| 5000/5000 [03:20<00:00, 24.90it/s, loss=1.12]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.4008, Top1Accuracy: 4099/10000 (41%) Top5Accuracy: 7025.0/10000 (70%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 16: 100%|██████████| 5000/5000 [03:15<00:00, 25.59it/s, loss=0.573]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.4544, Top1Accuracy: 4111/10000 (41%) Top5Accuracy: 6993.0/10000 (70%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 17: 100%|██████████| 5000/5000 [03:14<00:00, 25.71it/s, loss=0.467]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.4627, Top1Accuracy: 4102/10000 (41%) Top5Accuracy: 7044.0/10000 (70%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 18: 100%|██████████| 5000/5000 [03:09<00:00, 26.38it/s, loss=1.11]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.4664, Top1Accuracy: 4070/10000 (41%) Top5Accuracy: 7020.0/10000 (70%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 19: 100%|██████████| 5000/5000 [03:11<00:00, 26.09it/s, loss=0.979]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.5376, Top1Accuracy: 4123/10000 (41%) Top5Accuracy: 7099.0/10000 (71%)\n",
      "\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "lossv = []\n",
    "top1AccuracyList = []   # top1准确率列表\n",
    "top5AccuracyList = []   # top5准确率列表\n",
    "max_epoch = 20\n",
    "for epoch in range(max_epoch):  # loop over the dataset multiple times\n",
    "    train(epoch)\n",
    "    with torch.no_grad():\n",
    "        validate(lossv,top1AccuracyList,top5AccuracyList)\n",
    "        \n",
    "print('Finished Training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "78f4f553",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-24T04:13:28.043453Z",
     "iopub.status.busy": "2023-05-24T04:13:28.043075Z",
     "iopub.status.idle": "2023-05-24T04:13:28.047704Z",
     "shell.execute_reply": "2023-05-24T04:13:28.046813Z"
    },
    "executionInfo": {
     "elapsed": 22810,
     "status": "ok",
     "timestamp": 1684424352765,
     "user": {
      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
     },
     "user_tz": -480
    },
    "id": "Hag0zV9cmHm_",
    "outputId": "1434ac5c-f76c-4637-9223-e6955c942441",
    "papermill": {
     "duration": 11.322444,
     "end_time": "2023-05-24T04:13:28.049606",
     "exception": false,
     "start_time": "2023-05-24T04:13:16.727162",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# from google.colab import drive\n",
    "# drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c327846f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-24T04:13:49.709785Z",
     "iopub.status.busy": "2023-05-24T04:13:49.709415Z",
     "iopub.status.idle": "2023-05-24T04:13:50.494543Z",
     "shell.execute_reply": "2023-05-24T04:13:50.493650Z"
    },
    "id": "Z1-D3LkhvzDq",
    "papermill": {
     "duration": 11.557867,
     "end_time": "2023-05-24T04:13:50.496491",
     "exception": false,
     "start_time": "2023-05-24T04:13:38.938624",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'validation top5 accuracy')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wu6juO6wQEZFx6rDh5qa0RC8PBYQAYlNuGrocIiJqRR023ABgLE9NEhGZpA4dbtVTAg5evIkKtcbA1RARUWvp0OHW18senWwsUKiqxMm0O4Yuh4iIWkmHDjepVKLtve0/z1OTRESmokOHG8BxNyIiU9Thw21YdyeYyyS4eqsYqbeKDV0OERG1gg4fbnaW5nigiyMA9t6IiExFhw83ABgT6AqAt+IiIjIVDDf8cbeS+Kt3UFhWYeBqiIiopRhuALo42aCrkw0qNQKHL/EBpkRExo7hdk91740PMCUiMn46hVtkZCSCgoKgUCigUCgQHByM3bt3N7hOXl4ewsLC4O7uDrlcDn9/f+zatatFRbeFMfeezh2bkguNhg8wJSIyZma6NPby8sKqVavQvXt3CCGwceNGTJkyBYmJiejVq1et9uXl5Rg3bhxcXFywbds2eHp6Ij09Hfb29q1Vf6sZ2NkRdnIz3Coqx+/X89HP297QJRERUTPpFG6TJ0+u8f6dd95BZGQkjh8/Xme4ff3117hz5w6OHj0Kc3NzAEDnzp2bX20bMpdJMcLfGTuTs7H//A2GGxGREWv2mJtarUZUVBSKi4sRHBxcZ5uffvoJwcHBCAsLg6urK3r37o2VK1dCrVY3uG2VSoWCgoIaL33guBsRkWnQOdySk5Nha2sLuVyO+fPnIzo6Gj179qyz7dWrV7Ft2zao1Wrs2rULy5cvxwcffIC33367wX1ERERAqVRqX97e3rqW2SyjApwhkQBnswqQk1+ml30SEVHrkwghdLp6ory8HBkZGcjPz8e2bdvwn//8B3FxcXUGnL+/P8rKypCamgqZTAYAWLNmDVavXo3s7Ox696FSqaBS/fF07IKCAnh7eyM/Px8KhUKXcnU27fMjSMzIQ8SjfTDjAZ823RcRETVdQUEBlEplk7JA556bhYUFunXrhgEDBiAiIgJ9+/bFRx99VGdbd3d3+Pv7a4MNAHr06IGcnByUl5fXuw+5XK69IrP6pS/VN1Lex6cEEBEZrRbPc9NoNDV6WX82dOhQXL58GRrNHw8CvXjxItzd3WFhYdHSXbeJ6ltxHbl8C2UVDY8NEhFR+6RTuIWHh+PgwYNIS0tDcnIywsPDERsbi5kzZwIAQkNDER4erm2/YMEC3LlzB4sWLcLFixexc+dOrFy5EmFhYa37KVpRD3c7uCstUVqhxrGrtw1dDhERNYNOUwFyc3MRGhqK7OxsKJVKBAUFYe/evRg3bhwAICMjA1LpH3np7e2NvXv3YsmSJQgKCoKnpycWLVqEpUuXtu6naEUSSdUDTDfHZ+DAhVyMDnAxdElERKQjnS8oMQRdBhFbw77zNzBn4yl42lvh8NLRkEgkbb5PIiJqWJteUNIRDPFzgtxMiut5pbh4o8jQ5RARkY4YbnWwspBhiF8nAHyAKRGRMWK41WNMDz7AlIjIWDHc6lF9K67T6Xdxt7j+OXlERNT+MNzq4WlvhUA3O2gEEHfxpqHLISIiHTDcGlDde+O4GxGRcWG4NWDsnx5gWqnWNNKaiIjaC4ZbA/p5O8DB2hwFZZU4nX7X0OUQEVETMdwaIJNKMCqApyaJiIwNw60RHHcjIjI+DLdGjPB3hkwqwaXcImTcLjF0OURE1AQMt0Yorcxxv68DAE7oJiIyFgy3Jqi+anJ/Cue7EREZA4ZbE1Q/wPT4ldsoVlUauBoiImoMw60J/Jxt4NvJGuVqDQ5fvmXocoiIqBEMtyaQSCTah5buP8+rJomI2juGWxNVj7sdSMmFRtPun+9KRNShMdya6IEujrCxkCG3UIWzWQWGLoeIiBqgU7hFRkYiKCgICoUCCoUCwcHB2L17d5PWjYqKgkQiwdSpU5tTp8HJzWQY3t0ZALCPUwKIiNo1ncLNy8sLq1atwunTp3Hq1CmMGTMGU6ZMwdmzZxtcLy0tDa+88gqGDx/eomINjXcrISIyDjqF2+TJkzFp0iR0794d/v7+eOedd2Bra4vjx4/Xu45arcbMmTPxxhtvoGvXri0u2JBGBVb13H7PzEduYZmBqyEiovo0e8xNrVYjKioKxcXFCA4Orrfdm2++CRcXF8yZM6e5u2o3XOws0ddLCQCIvcAJ3URE7ZWZriskJycjODgYZWVlsLW1RXR0NHr27Fln28OHD2PdunVISkrSaR8qlQoqlUr7vqCg/VzAMTrQBb9l5mPfhRt4YqC3ocshIqI66NxzCwgIQFJSEuLj47FgwQLMmjUL586dq9WusLAQTz/9NL766is4OTnptI+IiAgolUrty9u7/YRISI+qu5XEXbyJvJJyA1dDRER1kQghWjRpKyQkBH5+fli7dm2N5UlJSejfvz9kMpl2mUZT9TRrqVSKlJQU+Pn51bnNunpu3t7eyM/Ph0KhaEm5LSaEwMSPDuFCTiFenRSIuSPq/gxERNS6CgoKoFQqm5QFLZ7nptFoagRRtcDAQCQnJyMpKUn7euSRRzB69GgkJSU12BuTy+Xa6QbVr/ZCIpHgH0M7AwC+OZYONSd0ExG1OzqNuYWHh2PixInw8fFBYWEhNm/ejNjYWOzduxcAEBoaCk9PT0RERMDS0hK9e/eusb69vT0A1FpubKb080TE7gvIvFuK/RdyMa6nq6FLIiKiP9Gp55abm4vQ0FAEBARg7NixOHnyJPbu3Ytx48YBADIyMpCdnd0mhbYnluYyTL93McnGo2mGLYaIiGpp8ZibPuhynlVfMu+WYMR7B6ARwK8vjUA3FztDl0REZNL0OubWUXk5WGuvnNx4NN3A1RAR0Z8x3Fpg9pDOAIAfEjJRUFZh2GKIiEiL4dYCwX6d4O9qi5JyNbadyjR0OUREdA/DrQUkEglCgzsDAL45lsbnvBERtRMMtxaa1t8TdpZmSLtdgriLvN8kEVF7wHBrIRu5GZ64v2pawAZOCyAiahcYbq0gNNgXEknV/Sav3iwydDlERB0ew60V+HayweiAqgeZfnOM0wKIiAyN4dZKZt2bFrDtdCaKVJWGLYaIqINjuLWS4d2c0NXJBkWqSmxP4LQAIiJDYri1EqlUgtBgXwBV95s0gruaERGZLIZbK3psgBdsLGS4crMYhy/fMnQ5REQdFsOtFdlZmuPxAV4A+LQAIiJDYri1stB7F5bsu5CLjNslhi2GiKiDYri1Mj9nWwzv7gQhgG+Ppxm6HCKiDonh1gb+MbQzAGDLyWsoKee0ACIifWO4tYFR/i7w7WSNgrJK/JiYZehyiIg6HIZbG5BKJXh6MKcFEBEZCsOtjfztfm9YmcuQcqMQx67eNnQ5REQdik7hFhkZiaCgICgUCigUCgQHB2P37t31tv/qq68wfPhwODg4wMHBASEhIThx4kSLizYGSitzPHqfJwBOCyAi0jedws3LywurVq3C6dOncerUKYwZMwZTpkzB2bNn62wfGxuLGTNm4MCBAzh27Bi8vb0xfvx4XL9+vVWKb++q7zcZc+4GMu9yWgARkb5IRAsHhBwdHbF69WrMmTOn0bZqtRoODg749NNPERoa2uR9FBQUQKlUIj8/HwqFoiXl6t1TXx3H0Su3MX+kH5ZNDDR0OURERkuXLGj2mJtarUZUVBSKi4sRHBzcpHVKSkpQUVEBR0fHBtupVCoUFBTUeBmr6t5b1MkMlFWoDVsMEVEHoXO4JScnw9bWFnK5HPPnz0d0dDR69uzZpHWXLl0KDw8PhISENNguIiICSqVS+/L29ta1zHYjpIcrPO2tkFdSgZ+SOC2AiEgfdA63gIAAJCUlIT4+HgsWLMCsWbNw7ty5RtdbtWoVoqKiEB0dDUtLywbbhoeHIz8/X/u6du2armW2GzKpBE/fe1rABk4LICLSixaPuYWEhMDPzw9r166tt83777+Pt99+G7/++ivuv/9+nfdhzGNuAHC3uByDI/ZBVanB1vnBGNi54dOyRERUm17G3KppNBqoVKp6v/7ee+/hrbfewp49e5oVbKbAwcYCU/tVTQvYwGkBRERtTqdwCw8Px8GDB5GWlobk5GSEh4cjNjYWM2fOBACEhoYiPDxc2/7dd9/F8uXL8fXXX6Nz587IyclBTk4OioqKWvdTGIHqC0v2nMlBTn6ZYYshIjJxOoVbbm4uQkNDERAQgLFjx+LkyZPYu3cvxo0bBwDIyMhAdna2tn1kZCTKy8vx+OOPw93dXft6//33W/dTGIGeHgo80NkRao3Af+PTDV0OEZFJa/GYmz4Y+5hbtZ2/ZyNscwKcbC1wZNkYyM1khi6JiMho6HXMjZpufC9XuCkscauoHDt/z258BSIiahaGmx6Zy6T4+2AfALzfJBFRW2K46dmTD/jAQibFb5n5SMy4a+hyiIhMEsNNz5xs5Zjc1wMAe29ERG2F4WYAs+9NC9iZnI3cQk4LICJqbQw3A+jjpcR9PvaoUAtsjs8wdDlERCaH4WYg1ZO6/xufgfJKjWGLISIyMQw3A5nY2x3OdnLcLFThf7/xaQFERK2J4WYgFmZS7djbB7+k8FlvREStiOFmQHOGdYGH0hJZ+WX4z6Grhi6HiMhkMNwMyNJchqUTAwEAn8dewY0CXjlJRNQaGG4G9khfD/T3sUdJuRqr96YYuhwiIpPAcDMwiUSCfz3cEwDwQ0ImkjPzDVwREZHxY7i1A/19HDC1nweEAN76+RyM4EENRETtGsOtnfjng4GwNJfiRNod7DmTY+hyiIiMGsOtnfCwt8LcEX4AgJW7z3NqABFRCzDc2pH5I7vCVSHHtTulWH8kzdDlEBEZLYZbO2JtYYZ/TqiaGvDZgcu4WagycEVERMZJp3CLjIxEUFAQFAoFFAoFgoODsXv37gbX2bp1KwIDA2FpaYk+ffpg165dLSrY1E3r74kgLyWKVJVYE8OpAUREzaFTuHl5eWHVqlU4ffo0Tp06hTFjxmDKlCk4e/Zsne2PHj2KGTNmYM6cOUhMTMTUqVMxdepUnDlzplWKN0VS6R9TA6JOXsO5rAIDV0REZHwkooXXnTs6OmL16tWYM2dOra9Nnz4dxcXF+Pnnn7XLBg8ejH79+uGLL75o8j4KCgqgVCqRn58PhULRknKNRtjmBOz8PRvBXTth83ODIJFIDF0SEZFB6ZIFzR5zU6vViIqKQnFxMYKDg+tsc+zYMYSEhNRYNmHCBBw7dqy5u+0wlj0YCAszKY5dvY2YczcMXQ4RkVHROdySk5Nha2sLuVyO+fPnIzo6Gj179qyzbU5ODlxdXWssc3V1RU5Ow/O4VCoVCgoKarw6Gm9Hazw7rAsAYOWu83zmGxGRDnQOt4CAACQlJSE+Ph4LFizArFmzcO7cuVYtKiIiAkqlUvvy9vZu1e0bi+dHd4OTrRxpt0vwzbE0Q5dDRGQ0dA43CwsLdOvWDQMGDEBERAT69u2Ljz76qM62bm5uuHGj5im1GzduwM3NrcF9hIeHIz8/X/u6du2armWaBFu5Gf5vgj8A4KN9l3C7iFMDiIiaosXz3DQaDVSqun/pBgcHY9++fTWWxcTE1DtGV00ul2unG1S/OqrHB3ijp7sChWWV+PDXS4Yuh4jIKOgUbuHh4Th48CDS0tKQnJyM8PBwxMbGYubMmQCA0NBQhIeHa9svWrQIe/bswQcffIALFy7g9ddfx6lTp7Bw4cLW/RQmTCaVYPm9qQH/jU9HSk6hgSsiImr/dAq33NxchIaGIiAgAGPHjsXJkyexd+9ejBs3DgCQkZGB7OxsbfshQ4Zg8+bN+PLLL9G3b19s27YNP/74I3r37t26n8LEBft1woRertAI4O2dfGoAEVFjWjzPTR864jy3v0q/XYyQNXGoUAusnz0QowNdDF0SEZFe6WWeG+mXbycb/GNo1dSAt3aeQ4WaUwOIiOrDcDMiC8d0QycbC1y9WYxNx9MNXQ4RUbvFcDMiCktzvDS+amrAh79eQl5JuYErIiJqnxhuRmb6/d4IcLVDfmkFpwYQEdWD4WZkzGRSvPZwDwDApuPpuJxbZOCKiIjaH4abERre3RljA11QqRFYueu8ocshImp3GG5G6tWHesBMKsH+C7k4ePGmocshImpXGG5Gys/ZFqHBnQFUTeyu5NQAIiIthpsRWzS2O+ytzXHxRhG+O9kxby5NRFQXhpsRU1qbY0lI1dSANb+kIL+0wsAVERG1Dww3I/fUIB/4OdvgbkkFPtnHqQFERADDzeiZy6R47d5TA9YdScXO37MbWYOIyPQx3EzA6AAXPDXIB0IAi7ck8upJIurwGG4m4q0pvfFQH3dUqAXmfXsap9PvGLokIiKDYbiZCJlUgn9P74cR/s4orVDjH+tP4nx2gaHLIiIyCIabCbEwk+KLv9+HAb4OKCirxNPrTiDtVrGhyyIi0juGm4mxtjDD17MGItDNDreKVPj7unjk5JcZuiwiIr1iuJkgpbU5vp0zCJ07WSPzbimeXhePu8V8PA4RdRwMNxPlbCfHt3MGwVUhx6XcIsxefwJFqkpDl0VEpBc6hVtERAQGDhwIOzs7uLi4YOrUqUhJSWl0vQ8//BABAQGwsrKCt7c3lixZgrIynipra96O1tg0ZxAcrM3xW2Y+5n5zCmUVakOXRUTU5nQKt7i4OISFheH48eOIiYlBRUUFxo8fj+Li+i9a2Lx5M5YtW4YVK1bg/PnzWLduHbZs2YJXX321xcVT47q72mHDPx6AjYUMR6/cxovfJfImy0Rk8iRCCNHclW/evAkXFxfExcVhxIgRdbZZuHAhzp8/j3379mmXvfzyy4iPj8fhw4ebtJ+CggIolUrk5+dDoVA0t9wO7eiVW5i9/iTKKzV47D4vrH48CFKpxNBlERE1mS5Z0KIxt/z8fACAo6NjvW2GDBmC06dP48SJEwCAq1evYteuXZg0aVJLdk06GuLnhE9n9IdMKsEPCZl4a+c5tODvGiKids2suStqNBosXrwYQ4cORe/evett99RTT+HWrVsYNmwYhBCorKzE/PnzGzwtqVKpoFKptO8LCjgZuTWM7+WG9x4Lwstbf8P6I2mwt7LAopDuhi6LiKjVNbvnFhYWhjNnziAqKqrBdrGxsVi5ciU+//xzJCQkYPv27di5cyfeeuuteteJiIiAUqnUvry9vZtbJv3FYwO8sGJy1Y2W//3rRaw/kmrgioiIWl+zxtwWLlyIHTt24ODBg+jSpUuDbYcPH47Bgwdj9erV2mWbNm3C3LlzUVRUBKm0dr7W1XPz9vbmmFsr+vDXi/jw16pH5Kx5oi8evc/LwBURETVMlzE3nU5LCiHwwgsvIDo6GrGxsY0GGwCUlJTUCjCZTKbdXl3kcjnkcrkupZGOFo3tjrySCmw4mob/2/Y77CzNMa6nq6HLIiJqFTqdlgwLC8OmTZuwefNm2NnZIScnBzk5OSgtLdW2CQ0NRXh4uPb95MmTERkZiaioKKSmpiImJgbLly/H5MmTtSFH+ieRSPCvh3vi0fs8odYIhG1OwNErtwxdFhFRq9Cp5xYZGQkAGDVqVI3l69evx+zZswEAGRkZNXpqr732GiQSCV577TVcv34dzs7OmDx5Mt55552WVU4tJpVK8N5jQSgsq0TMuRt4buMpfDd3MIK87A1dGhFRi7Ronpu+cJ5b2yq794icY1dvw8HaHN/PC0Z3VztDl0VEVIPe5rmRabA0l+GrWfejr5cSd0sq8PS6Ezh48SbUmnb/dw8RUZ3YcyOtO8XlmL72GC7lFgEAXBVyTO3vicfu84I/e3JEZGC6ZAHDjWq4WajCx/su4affspBfWqFd3ttTgUf7e+GRfh5wsuWVrESkfww3ajFVpRoHLtzE9oRM7L+Qi8p7pyhlUglG+Tvj0fu8MLaHCyzNecUrEekHw41a1Z3icvz8exZ+OJ2J3zLztcvtLM3wcJAHHrvPEwN8HSCR8EbMRNR2GG7UZi7nFmJ7wnVEJ15Hdv4fz+Tz7WSNaf098Wh/L/h0sjZghURkqhhu1OY0GoHjV29je+J17E7ORnH5Hw9BHdjZAY/e54VJfdyhtDI3YJVEZEoYbqRXJeWV+OXsDfyQkInDl2+h+jvKwkyKyUEeCJ8UyItQiKjFGG5kMDn5ZdiRdB0/JGTi4o2qKQWONhZ4c0ovPBzkYeDqiMiYMdzI4IQQSMjIw/+LTsaFnEIAwKQ+bnhzSm/24oioWXiHEjI4iUSCAb4O+GnhMLw4tjvMpBLsSs7BuDVx+N9vWXwKOBG1KYYbtSkLMyleGuePH8OGItDNDndLKvDCd4lYsCkBNwtVjW+Amqy8UoNNx9ORcq+nTNSRMdxIL3p7KvHTwmFYdK8Xt+dsDsb/Ow4/sRfXKsoq1Jj37Sm89uMZPLPhJMorNYYuicigGG6kNxZmUiwZ548dC4eih7sCd0sq8OJ3iZi/6TRyC8sa3wDVqaS8EnM2nsSBlJsAgOt5pdh2OtPAVREZFsON9K6XhxI7woZicUhVL27v2RsY/++D2JF0nb04HRWWVSB03QkcuXwbNhYyPHqfJwDgswOX2XujDo3hRgZhYSbF4hB//LRwGHq6K5BXUoFFUUmY9y17cU2VV1KOv/8nHqfS70JhaYZvnx2EldP6wMVOjut5pfj+1DVDl0hkMAw3MqieHgrsWDgUL43zh7lMgl/O3cC4NQfxYyJ7cQ25VaTCk18ex2+Z+XCwNsfm5wbjPh8HWJrLsGCUH4Cq3puqUt3IlohME8ONDM5cJsWLY7vjp4XD0MtDgfzSCizekoTnvjmN3AL24v7qRkEZpq89hgs5hXCylWPLvGD09lRqvz7jAR+4KuTIzi/D9yfZe6OOieFG7UYPdwV+DBuKl+/14n49fwPj/n0Q0YmZ7MXdk3m3BE+sPYYrN4vhrrTE9/MG13qQrKW5DM+P6gYA+OzAFZRVsPdGHY9O4RYREYGBAwfCzs4OLi4umDp1KlJSUhpdLy8vD2FhYXB3d4dcLoe/vz927drV7KLJdJnLpHhhbHf874Vh6O1Z1YtbsuU3PPfNqQ4/Fpd2qxhPfHEM6bdL4O1ohe/nBaOrs22dbacP9IabwhI5BWXYwt4bdUA6hVtcXBzCwsJw/PhxxMTEoKKiAuPHj0dxcXG965SXl2PcuHFIS0vDtm3bkJKSgq+++gqenp4tLp5MV6CbAtHPD8Ur46t7cbl4+OPDOJ1+x9ClGcSlG4V4Yu0xZOWXoauzDbbOGwJvx/ofLWRpLkPY6Kqxt89jL7P3Rh1Oi+4tefPmTbi4uCAuLg4jRoyos80XX3yB1atX48KFCzA3b97jT3hvyY4tJacQCzcn4FJuEcxlEvzr4Z74+2DfDvNw1LNZ+Xh63QncKS5HoJsdvp0zCM52jd+fU1WpxujVscjKL8OKyT3xj6Fd9FAtUdvR270l8/Ornsrs6OhYb5uffvoJwcHBCAsLg6urK3r37o2VK1dCra7/L0mVSoWCgoIaL+q4Atzs8GPYUDzUxx0VaoHlO87ila2/d4jeSNK1PMz48jjuFJejj6cS3z03uEnBBgByMxmeH1019vZ5LMfeqGNpdrhpNBosXrwYQ4cORe/evettd/XqVWzbtg1qtRq7du3C8uXL8cEHH+Dtt9+ud52IiAgolUrty9vbu7llkomwkZvh06f649VJgZBKgB8SMvFY5FFcu1Ni6NLazInUO/j7f+JRUFaJAb4O+O9zg+BgY6HTNp643xue9la4WajCf+Mz2qhSovan2aclFyxYgN27d+Pw4cPw8vKqt52/vz/KysqQmpoKmUwGAFizZg1Wr16N7OzsOtdRqVRQqf64qW5BQQG8vb15WpIAAEcv38LC7xJxp7gcDtbm+HhGfwzv7mzoslrV4Uu38Ow3J1FWoUFw1074z6z7YSM3a9a2Nsdn4NXoZDjZynHon6NhZSFr5WqJ9KPNT0suXLgQP//8Mw4cONBgsAGAu7s7/P39tcEGAD169EBOTg7Ky8vrXEcul0OhUNR4EVUb0s0J/3thGIK8lLhbUoFZX5/A57GXTWa6wL7zN/DMxqpgGxXgjPX/GNjsYAOAxwd4wdPeCreKVPhvfHorVkrUfukUbkIILFy4ENHR0di/fz+6dGl8gHro0KG4fPkyNJo/7nN38eJFuLu7w8JCt1MsRNU87asuhX/ifi9oBPDenhQs2JSAIlWloUtrkV3J2Zj37WmUV2owoZcr1j49AJbmLetpWZhJ8cKYqrG3L+KuoKTcuI8RUVPoFG5hYWHYtGkTNm/eDDs7O+Tk5CAnJwelpaXaNqGhoQgPD9e+X7BgAe7cuYNFixbh4sWL2LlzJ1auXImwsLDW+xTUIVmay/DuY0FYOa0PzGVVj9GZ8ulhXM4tMnRpzRKdmImFmxNQqRF4pK8HPn3qPsjNWucU4mMDvODtaIVbReXYdJy9NzJ9OoVbZGQk8vPzMWrUKLi7u2tfW7Zs0bbJyMioMZbm7e2NvXv34uTJkwgKCsKLL76IRYsWYdmyZa33KajDkkgkeGqQD7bMC4abwhJXbhZj6mdHsOdMjqFL08l3JzLw0ve/QSOAJ+73wr+n94O5rPVuIGQuk+KF0d0BAGvjrrL3RiavRfPc9IXz3KgpbhaqsHBzAuJTqyZ6h432w0vjAiCTtv58uMy7JUjMyEOxqhKVGgG1RqBCrYFaI1CpEahUC1RqNPf+rfnTsqr3ao1AhUZArdGgpFyN2HvPYgsN9sXrk3tB2gY1V6g1GPtBHDLulGDZxEDMH+nX6vsgaku6ZAHDjUxKhVqDVbsvYN3hVADA8O5O+PjJ/jpfQv9nQghcu1OK46m3EX/1DuJTbyPzbmnjK+po7oiuCJ8Y2KaT07eeuob/2/Y7HG0scOifo1t0oQqRvjHcqMPbkXQdy35IRmmFGl4OVvji7wNq3Dm/IUIIpN4qRnzqHcRfvY341DvIzq95X0uZVILeHgo42cphJpPATCqFmUwCmVQCc6kUMpkE5lIJZPeWm0nvvWTSqjayqq+Z31uncycbDPHr1OZ3XalUaxCyJg5pt0vwzwcDtDdYJmpL1+6UYNWeC5g5yAdD/JyavR2GGxGA89kFmL/pNNJvl0BuJsXKaX3w2IDaU1eEELhyswjHr97RBlpuoapGG3OZBEFe9hjUxRGDunbCAF8H2Bppr+eH05l4eetvsLc2x+GlY4z2c1D7V6SqRGTsZXx1KBXllRr08VTip4VDm/1HHMON6J78kgos3pKIA38a0/p/D/Wo6pndO8V4IvUObhXVnHNpIZOin7c9BnetCrP7fBxMZvJzpVqDcf8+iNRbxfi/CQEIG83eG7UujUZgW0ImVu9Nwc17fygGd+2E5Q/3RE+P5v8OZ7gR/YlGI/DRvkv4aN8lAFXzvsorNTXayM2kuM/HAYO6OmJQl07o72Pf4vll7Vl0YiaWbKnqvR3652jYWTbvpuZEf3X86m289fM5nM2quiewbydr/L9JPTCup2uLT7vrkgU8H0EmTyqVYMk4fwR5KbF4SxIKyyphZS7D/Z0dtKcZg7yUrTanzBg80tcTn+y/jKs3i7HxaBoWjulu6JLIyGXcLkHE7vPYfW8ajp2lGV4c0x2hQ3wN8rPFnht1KPklFbh2twQBbnatOo/MGO1Iuo5FUUlQWpnj0NLRULD3Rs1QWFaBTw9cxvrDaShXayCVAE8N8sGSEH90sm3aEyyaij03onoorc2htG7aVZOm7uEgD3yy/zIu5xZhw5E0vDiWvTdqOrVG4PtT1/DBLynaMevh3Z3w2kM9EeBmZ+DqWvg8NyIyXjKpRBto/zl0FfmlFQauiIzF0cu38NDHhxC+PRm3isrR1ckGX8++H98880C7CDaA4UbUoT3Uxx3dXWxRUFaJ9UdSDV1OgyrUGly7UwK1pt2PpJis1FvFeO6bU3jqP/G4kFMIhaUZ/vVwT+xdMgJjAlt+wUhr4mlJog5MJpVgUUh3LNyciHWHU/GPoV2gtGpfY28Vag2iE67jkwOXcO1OKWzlZujvY4+BnR1xf2cH9Pc2nWka7VV+aQU+3X8JG46moUItIJNK8PRgXywa271Fd/9pSww3og5uUm93BLheRsqNQqw7nIqXxvkbuiQAVfPxtidex6f7LyPj3hPXJZKqicGHLt3CoUu3AABmUgl6eSox0NcB998LPKdWvpCho6pUaxB18hrWxFzEneKqcbVRAc547aEe6ObSPk4/1odXSxIRdiVn4/n/JsBObobDS8dAaW243lulWoPoxOv49MBlpN+uCjUnWwvMG+GHGYN8kHG7BKfS7+Bk2l2cTL2DnIKyWtvo6mSD+ztXhd3Azo7o3Mm6XZ0yaw1CCBSqKpFfUoH80goUlFb9N7+0AgVlFVBVaKCq1EBVqUZ5ZdW/q/9b93I1ytUa7XrVy6rPAndzscX/e6gHRge4GOwzcxI3EelEoxGY9PEhXMgpxAtjuuHl8QF6r6FSrcGOpCx8sv8S0u6FWicbC8wb2RV/H+wLa4vaJ5qEELieV4pTaXdxMu0OTqXdRcqNwlrtnGwtcL9vVa9uYGdH9PRQtLupILkFZbh8s6hGSOWXViCv5E+hVSPAKvUy/uhgbY4l4/wx4wEfgx8zhhsR6WzPmWzM35QAW7kZDi8dDXtr/YylVKo1+Om3LHyy/zJSbxUDABxtLDB3RFeEBtcdag3JKylHQsZdnEy7i1Npd/DbtXyUq2vekcbKXIZh3Z0wc5APRnR3bpNHDDWFEAInUu9gw9E0/HLuRrPCysJMCqWVufZlb2UOhZU5LM2lkJvJYGEmhdxMCguZFPK/LjOr+f7Py6rf21tbwMKsffwhwHAjIp1pNAIPfXIY57MLEDbaD/83IbBN96fWCPz023V8su8yrt4LNQdrc8wd4YfQYN9WexxPWYUaZ67na8PuVPrdGtMevB2t8NQDvnjifq9Wn3TcUE0/JWVh/dE0nM8u0C7v6mQDBxuLGmGl+EtwKa3Na3zdlG8T91cMNyJqlr1nczDv29OwsZDh0NIxcGyDK+HUGoH//ZaFj/dfwtWbVaFmb22OuSO6YlZw5zZ/xpxGI3A+pwBbT2Xih4RMFJZVPZXcQibFxD5umDnIFwM7O7TJGF1WXim+PZ6OqBMZuFtSFbCW5lJM6++F2UM6t5s5Yu0Vw42ImkUIgYc/OYyzWQVYMMoPSx9svd6bWiPw8+9Z+HjfJVz5U6g9N7wrZg3pbJBH75SWq/G/37KwKT4dv2fma5cHuNph5mAfTOvv2eKbSlefetx4LA17z/5x6tHT3gqzhvjiifu99XYK2Ni1WbhFRERg+/btuHDhAqysrDBkyBC8++67CAho2uBzVFQUZsyYgSlTpuDHH39s6m4ZbkR6FHPuBp775hSszGUYE+gCKwsZbCxksJabwdr83n8tZLC2kMHG4t6/5WawsZDda2sGK4uqMRuJRAK1RmBncjY+3ncJl3OLAABKK3M8N7wLZg3p3G6eSPB7Zh7+ezwDO367jrKKqjE6awsZpvTzwMxBvk1+2G21+k49BnfthNlDOyOkhytkBhrrM1ZtFm4PPvggnnzySQwcOBCVlZV49dVXcebMGZw7dw42NjYNrpuWloZhw4aha9eucHR0ZLgRtVNCCEz97Ah++1NPpjlkUgmsLWSQSiTaMS6FpRmeG94Vs4e2n1D7q/zSCkQnZGJTfIY2jAGgn7c9Zg7yweS+Hg2Oc2XllWLT8XR8V8epx1lDfBHoxt9hzaW305I3b96Ei4sL4uLiMGLEiHrbqdVqjBgxAs888wwOHTqEvLw8hhtRO3azUIWDF2+iuLwSxSo1SsorUVJe9d+q9/f+Xa5G6V/aqP7yrDygKtSevRdqxvL0ASEE4lPvYNPxdOw9m4MKddWvSqWVOR4f4IWnBvnAz9lW2/Zk2l1sOJpa69RjaLAvpg/kqcfWoLenAuTnV/1l5+jo2GC7N998Ey4uLpgzZw4OHTrUkl0SkR4428nx2ACvZq1bqdagpEKN0nI1ilWVKK1Qo3Mnmza/UKS1SSQSDO7aCYO7dsLNQhW+P3UNm+MzcD2vFOsOp2Ld4VQM8euEEf7O+CkpC+d46rFdafZ3m0ajweLFizF06FD07t273naHDx/GunXrkJSU1ORtq1QqqFQq7fuCgoIGWhNRe2Imk0IhkxpND60pnO3kCBvdDfNH+uHgxZvYdDwd+1NycfTKbRy9chsATz22N80Ot7CwMJw5cwaHDx+ut01hYSGefvppfPXVV3BycmrytiMiIvDGG280tzQiojYhk0owOtAFowNdkHm3BFEnriHx2l2M6O7MU4/tTLPG3BYuXIgdO3bg4MGD6NKlS73tkpKS0L9/f8hkfwy+ajRV5+OlUilSUlLg5+dXa726em7e3t4ccyMi6sDabMxNCIEXXngB0dHRiI2NbTDYACAwMBDJyck1lr322msoLCzERx99BG9v7zrXk8vlkMt5V28iImoencItLCwMmzdvxo4dO2BnZ4ecnBwAgFKphJWVFQAgNDQUnp6eiIiIgKWlZa3xOHt7ewBocJyOiIioJXQKt8jISADAqFGjaixfv349Zs+eDQDIyMiAVNo+brJJREQdE2+/RURERkGXLGAXi4iITA7DjYiITA7DjYiITI5R3A+neliQdyohIuq4qjOgKZeKGEW4FRYWAkC98+KIiKjjKCwshFLZ8COIjOJqSY1Gg6ysLNjZ2bXJ03HbQvVdVa5du2ZUV3gaa92A8dZurHUDxls769a/1qhdCIHCwkJ4eHg0OuXMKHpuUqkUXl7Nu0O5oSkUCqP7JgSMt27AeGs31roB462ddetfS2tvrMdWjReUEBGRyWG4ERGRyWG4tRG5XI4VK1YY3Q2gjbVuwHhrN9a6AeOtnXXrn75rN4oLSoiIiHTBnhsREZkchhsREZkchhsREZkchhsREZkchlszREREYODAgbCzs4OLiwumTp2KlJSUBtfZsGEDJBJJjZelpaWeKv7D66+/XquOwMDABtfZunUrAgMDYWlpiT59+mDXrl16qvYPnTt3rlW3RCJBWFhYne0NdbwPHjyIyZMnw8PDAxKJBD/++GONrwsh8K9//Qvu7u6wsrJCSEgILl261Oh2P/vsM3Tu3BmWlpYYNGgQTpw4odfaKyoqsHTpUvTp0wc2Njbw8PBAaGgosrKyGtxmc77fWrNuAJg9e3atGh588MFGt9vWx7yxuuv6fpdIJFi9enW929TH8W7K77+ysjKEhYWhU6dOsLW1xWOPPYYbN240uN3m/mzUh+HWDHFxcQgLC8Px48cRExODiooKjB8/HsXFxQ2up1AokJ2drX2lp6frqeKaevXqVaOOw4cP19v26NGjmDFjBubMmYPExERMnToVU6dOxZkzZ/RYMXDy5MkaNcfExAAA/va3v9W7jiGOd3FxMfr27YvPPvuszq+/9957+Pjjj/HFF18gPj4eNjY2mDBhAsrKyurd5pYtW/DSSy9hxYoVSEhIQN++fTFhwgTk5ubqrfaSkhIkJCRg+fLlSEhIwPbt25GSkoJHHnmk0e3q8v3W2nVXe/DBB2vU8N133zW4TX0c88bq/nO92dnZ+PrrryGRSPDYY481uN22Pt5N+f23ZMkS/O9//8PWrVsRFxeHrKwsPProow1utzk/Gw0S1GK5ubkCgIiLi6u3zfr164VSqdRfUfVYsWKF6Nu3b5PbP/HEE+Khhx6qsWzQoEFi3rx5rVyZbhYtWiT8/PyERqOp8+vt4XgDENHR0dr3Go1GuLm5idWrV2uX5eXlCblcLr777rt6t/PAAw+IsLAw7Xu1Wi08PDxEREREm9QtRO3a63LixAkBQKSnp9fbRtfvt5aqq+5Zs2aJKVOm6LQdfR/zphzvKVOmiDFjxjTYRt/HW4jav//y8vKEubm52Lp1q7bN+fPnBQBx7NixOrfR3J+NhrDn1gry8/MBAI6Ojg22Kyoqgq+vL7y9vTFlyhScPXtWH+XVcunSJXh4eKBr166YOXMmMjIy6m177NgxhISE1Fg2YcIEHDt2rK3LrFd5eTk2bdqEZ555psEbabeX410tNTUVOTk5NY6nUqnEoEGD6j2e5eXlOH36dI11pFIpQkJCDPr/AKj6vpdIJLC3t2+wnS7fb20lNjYWLi4uCAgIwIIFC3D79u1627bHY37jxg3s3LkTc+bMabStvo/3X3//nT59GhUVFTWOX2BgIHx8fOo9fs352WgMw62FNBoNFi9ejKFDh6J37971tgsICMDXX3+NHTt2YNOmTdBoNBgyZAgyMzP1WC0waNAgbNiwAXv27EFkZCRSU1MxfPhw7WOF/ionJweurq41lrm6uiInJ0cf5dbpxx9/RF5eHmbPnl1vm/ZyvP+s+pjpcjxv3boFtVrd7v4flJWVYenSpZgxY0aDN8HV9futLTz44IP45ptvsG/fPrz77ruIi4vDxIkToVar62zfHo/5xo0bYWdn1+ipPX0f77p+/+Xk5MDCwqLWHz0NHb/m/Gw0xiieCtCehYWF4cyZM42e1w4ODkZwcLD2/ZAhQ9CjRw+sXbsWb731VluXqTVx4kTtv4OCgjBo0CD4+vri+++/b9Jfhe3BunXrMHHiRHh4eNTbpr0cb1NUUVGBJ554AkIIREZGNti2PXy/Pfnkk9p/9+nTB0FBQfDz80NsbCzGjh2rlxpa6uuvv8bMmTMbvShK38e7qb//DIE9txZYuHAhfv75Zxw4cEDnR/KYm5ujf//+uHz5chtV1zT29vbw9/evtw43N7daVznduHEDbm5u+iivlvT0dPz666949tlndVqvPRzv6mOmy/F0cnKCTCZrN/8PqoMtPT0dMTExOj+6pLHvN33o2rUrnJyc6q2hvR3zQ4cOISUlRefveaBtj3d9v//c3NxQXl6OvLy8Gu0bOn7N+dloDMOtGYQQWLhwIaKjo7F//3506dJF522o1WokJyfD3d29DSpsuqKiIly5cqXeOoKDg7Fv374ay2JiYmr0ivRp/fr1cHFxwUMPPaTTeu3heHfp0gVubm41jmdBQQHi4+PrPZ4WFhYYMGBAjXU0Gg327dun9/8H1cF26dIl/Prrr+jUqZPO22js+00fMjMzcfv27XpraE/HHKg6UzFgwAD07dtX53Xb4ng39vtvwIABMDc3r3H8UlJSkJGRUe/xa87PRlMKJR0tWLBAKJVKERsbK7Kzs7WvkpISbZunn35aLFu2TPv+jTfeEHv37hVXrlwRp0+fFk8++aSwtLQUZ8+e1WvtL7/8soiNjRWpqaniyJEjIiQkRDg5OYnc3Nw66z5y5IgwMzMT77//vjh//rxYsWKFMDc3F8nJyXqtW4iqK9Z8fHzE0qVLa32tvRzvwsJCkZiYKBITEwUAsWbNGpGYmKi9onDVqlXC3t5e7NixQ/z+++9iypQpokuXLqK0tFS7jTFjxohPPvlE+z4qKkrI5XKxYcMGce7cOTF37lxhb28vcnJy9FZ7eXm5eOSRR4SXl5dISkqq8X2vUqnqrb2x77e2rruwsFC88sor4tixYyI1NVX8+uuv4r777hPdu3cXZWVl9datj2Pe2PeKEELk5+cLa2trERkZWec2DHG8m/L7b/78+cLHx0fs379fnDp1SgQHB4vg4OAa2wkICBDbt2/Xvm/Kz4YuGG7NAKDO1/r167VtRo4cKWbNmqV9v3jxYuHj4yMsLCyEq6urmDRpkkhISNB77dOnTxfu7u7CwsJCeHp6iunTp4vLly/XW7cQQnz//ffC399fWFhYiF69eomdO3fqueoqe/fuFQBESkpKra+1l+N94MCBOr83qmvTaDRi+fLlwtXVVcjlcjF27Nhan8fX11esWLGixrJPPvlE+3keeOABcfz4cb3WnpqaWu/3/YEDB+qtvbHvt7auu6SkRIwfP144OzsLc3Nz4evrK5577rlaIWWIY97Y94oQQqxdu1ZYWVmJvLy8OrdhiOPdlN9/paWl4vnnnxcODg7C2tpaTJs2TWRnZ9fazp/XacrPhi74yBsiIjI5HHMjIiKTw3AjIiKTw3AjIiKTw3AjIiKTw3AjIiKTw3AjIiKTw3AjIiKTw3AjIiKTw3AjIiKTw3AjIiKTw3AjIiKTw3AjIiKT8/8BRLkOybYrqboAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6FGoAj5EREd1h//79uHjxIv7xj39g7Nix6NSpk6lLogYwyIiI7vD222/jt99+w8MPP4yPP/7Y1OVQI/AYGRERWTQeIyMiIovGICMiIotmdsfIdDodMjMz4ezs3OQRsomIyPIJIVBSUgIfHx9IpXVvd5ldkGVmZjbbGHdERGT50tPT4efnV+fjZhdk1YOnpqenN9tYd0REZHmKi4vh7+/f4OVzzC7IqncnyuVyBhkRETV4mImdPYiIyKIxyIiIyKIxyIiIyKIxyIiIyKIxyIiIyKIxyIiIyKIxyIiIqNkIIZCWr8IPZzOhKq9slWWa3XlkRERtgRAC6godSjWVUJVXQqnRQlleCaWmEqpyLVTlWthYSWBnY1V1s5bqf7e3sYKdjRSy2z9traRmO2RffqkG5zKKcCa9EGczCnE2vRAFqgoAwHd/jkC/INcWr4FBRkTUCEII7L2Ui7PphVCWV0J1RzApy7VQ3Z5WejuolOWVaK6LZEkkuB1ufwSezMYK9jZVv8vtbODXwR7+rg4GPx1sm/crvqxciwuZRTibXqgPrvRbZTXa2VpJ0d1HjgqtrlmXXxcGGRFRA46l3MJ7uy7hTHphk57vaGsFB5l11U9bazjJrGFna4VKrQ7qCi3UFdU/tVBXVv1eVqHVB6EQ0G/FGaOjoy38XB3g38Eefh0c4O9qD/8OVSHn28EeMmurOp+r1QlcyS25HVpV4ZWUUwKtrmY6B7s7ItzfBb38XRDu54IQb+d6593cGGRERHW4mluCf/6chLiLOQCqtoqeDPeBq5MtnGTWcLC1gqOtNRxkVnCUWVf9bmtV9Zis6jF7GytIpcbvFhRCoFyrg7pCB83tsCurDrs7Ak9doUWBshzpBWXIKFAh/VYZ0gtUKFFXIl9ZjnxlOc7WEsASCeDpbKffgvPvYA9PhR1S81U4k16ICzeKag1Od2cZet0RWj39FFDY2xj9+poTg4yI6C65xWos23sFm46nQScAK6kEE/v6Y97QLvCQ27VKDRKJBDJrq6otmyYERVFZBdJvqZBRoEJGQRnSb6kMwq6sQovsYjWyi9U4kVpQ6zwcba0Q5udye2tLgXB/F3jJ7czueN09Bdn777+PRYsWYe7cuVi+fDkAQK1W45VXXsHGjRuh0WgwfPhwrFixAp6ens1RLxFRiynVVOLzQ9fwxaFrKKuo2hp5/H5PLBzRDfd51D8Cu7lR2NtA4atAqK+ixmNCCOQry/UBl1FQtRWXVVgGHxd7hPu7oLe/Czq7O8GqCVuTra3JQXb8+HGsWrUKYWFhBtPnz5+Pn376CZs3b4ZCocCcOXMwfvx4HD58+J6LJSJqCRVaHTYeT8d/9v6OvNJyAEAvfxe8PrJ7q/S6a20SiQRuTjK4OVXtJrR0TQqy0tJSPPfcc/jiiy/w7rvv6qcXFRXhq6++wvr16/HYY48BAFavXo3u3bvjyJEjGDBgQPNUTURtlqZSi8NX87AnMQcVWoE+gR3Qt1MH3Ofh1Oy7tIQQiE3Mxgc/J+FanhIA0KmjA/46IgRRoV5mtwuNatekIIuJicGoUaMQGRlpEGQnT55ERUUFIiMj9dNCQkIQEBCAhIQEBhkR1UpdoUX87zex+3wW9l3KRYnmjxNpvz+VAQDo4GCDPoGu6NupAx7s5IqevgrYWjd9TIcT16t6Ip5KKwRQ1cNvbmQXPNMvADZWHCvCkhgdZBs3bsSpU6dw/PjxGo9lZ2fD1tYWLi4uBtM9PT2RnZ1d6/w0Gg00Go3+fnFxsbElEZEFUmoqcTDpJnZdyMKBy7kGPeQ85TJEhXrD2c4aJ64X4HR6AQpUFdh7KQd7L1X1IJRZS9HL3wV9O7niwU4d8EBgB8jtGu4UkXyzFB/8fBmxiVXzsbORYubAzpg1qDOcG/F8Mj9GBVl6ejrmzp2LuLg42Nk1T8+dpUuXYsmSJc0yLyJqmnMZhbhRUIYgd0d06ugIO5uWOQeoRF2B/Zdzset8Fg4m3YSm8o8TZn1d7BEV6oWonl7o7d/BoMt6hVaHxMxiHE+5hePXb+FEagFuKctxNOUWjqbcAgBIJUCIl1y/xda3kyu8FH98T90s0eA/+37HhmPp0OoEpBLg6Qf9Mf/xrvBspZ6I1DIkQjT+3PPt27dj3LhxsLL640Ou1WohkUgglUoRGxuLyMhIFBQUGGyVBQYGYt68eZg/f36Neda2Rebv74+ioiLI5fImviwiaqzPDyXjvV2XDab5KOwQ5O6IIDdHBLk5IcjNAUFuTvDrYG/0brciVQXiLuVg9/ks/HIlD+V3jPYQ2NEBUaHeiAr1QpifotHHpIQQuJanxInrt3D8egGOX7+F1HxVjXZ+HezRt5MrOjraYv2xNP1WX2R3DywcEYIunpbVE7G9KS4uhkKhaDAPjAqykpISpKamGkybNm0aQkJCsHDhQvj7+8Pd3R0bNmxAdHQ0ACApKQkhISGNPkbW2MKJ6N4IIfDffVexbO/vAIBuns7IKipDsbrugV6tpRIEuDogyM0Rndyqgq6zmyOC3B3h6Wyn34q6pSzHnsRs7LqQjd+u5qHyjtEgOrs7YlRPb4wI9cL93vJm61CRe/t8qOPXb+HE9QIkZhbh7kEowv0UWDSyOwZ07tgsy6SW1dg8MGrXorOzM0JDQw2mOTo6omPHjvrpM2bMwIIFC+Dq6gq5XI6XXnoJERER7OhBZEaEEPjnz0n4LD4ZAPDqsK6Y81gXCCFQoKpASl4prt1UIiVPiev5Sly7WfVTXaHDtTylvoffnexspOjU0RFOMmucTi80GMooxMu5asurpxe6tEDvQwDwkNthZE9vjOzpDaDqnLDTaQU4fr0A1/OUePx+TzwR5s2eiG1Qs4/ssWzZMkilUkRHRxucEE1E5kGnE3j7x4tY89t1AMAbo7rjhYGdAVSdX+TqaAtXR1f0CXSt8bzsYjWu3w6ylNu363lKpN1SQV2hw+XsEn37UF+5frdhZ3enVnt91Zxk1hjYxR0Du7i3+rKpdRm1a7E1cNciUcvR6gT+tu08Nh5PBwC8OzYUfxoQeM/zrdDqkFFQhpS8UuSXlqN/UEcEdHS45/lS+9YiuxaJyHJVanV4dfNZbD+TCakE+GBCOCb08WuWedtYSW93DHFslvkRGYNBRtQOlFfqMHfjaey+kA1rqQTLJ/XCE2E+pi6LqFkwyIjaOHWFFrO/PYkDSTdhayXFp889gMfv5yDe1HYwyIjaMFV5JWb+7wQOX82HnY0Unz//IAZ1ZecHalsYZERtVIm6AtPXHMfx6wVwtLXCV1P78vwpapMYZERtUKGqHFO+PoazGUVwtrPG2un98EBAB1OXRdQiGGREbUxeqQZ/+vIoLmeXoIODDb6Z0b/WiysStRUMMqI2JKdYjWe/OILkm0q4OcmwfmZ/dOV4gtTGMciI2oiMAhWe+/IoUvNV8FbYYd0L/U0yogZRa2OQEbUB1/OUePaLI8gsUsPf1R7rXxgAf1eOrEHtA4OMyMJdySnBc18eRW6JBp3dHbH+hQEG1+EiausYZEQWLDGzCM9/dQy3lOUI8XLGNzP6w91ZZuqyiFoVg4zIQpRqKpFVWIasIjWyisqQWajG6sMpKFZXIsxPgbXT+qGDo62pyyRqdQwyIjOg1FTqAyqrSI2swjt+v/2zpI4LXvYJ7IDV0/pCbmfTylUTmQcGGVELqtDqkFeqwc2SqltuiQa5xRpkF1cFVHaRGpmF9V+V+U7OdtbwUdjD28UO3go7BLs74dn+AXCw5Z8ytV/89FObpiqvRKm6EtZWUlhbSWAjrfppLZU0+UrBQgiUaiqRe0c4Vf1U6wOrevotZXmj5+sss4a3ix28FPbwUdjBW2EPb4WdPrS8FPZwkvFPluhu/KugNmvziXT8fccFqCt0tT5uLZXow83GWgprqQQ2Vn8E3R+/S2FjJYFWJ5BXWo7cEnWd86xrOW5OMnjIZXB3ksHdWQZPuR18XP4IKy+FHZy5a5CoSRhk1OYIIfDvuN/x8f6rAACJBKjtOuiVOoFKnYAaOkBj/HKcZNbwcJbBzVkGD+eqgPJwtrv9U6b/2cHBFlJp07b+iKhhDDJqUzSVWizccg7bz2QCAGIeDcYrj3cDAFTodKjUClRqBcq1OlTevl+h1aFSd/unVqBSp0N5pajxuASA++2AcneW8bgUkZngXyK1GYWqcsz65iSOpdyClVSC98aFYmLfAP3jMqkVeIiJqO3hnzW1CWn5KkxdcwzXbirhLLPGij89gIFdeAFJovaAQUYW71RaAWauPYF8ZTl8FHb4elpfhHjJTV0WEbUSBhlZtN3nszBv0xloKnUI9ZXjqyl94SnnOINE7QmDjCySEAJf/HINS3dfhhDAYyEe+PiZ3nDkQTCidod/9WRxKrU6vLUzEd8eSQMATI4IxJtP3A9rK6mJKyMiU2CQkUUp1VTipfWncCDpJiQS4G8ju2PGI0FNHqWDiCyfUf/Crly5EmFhYZDL5ZDL5YiIiMDu3bv1jw8ZMgQSicTg9uKLLzZ70dQ+ZRep8fRnCTiQdBN2NlKsfK4PXhjYmSFG1M4ZtUXm5+eH999/H126dIEQAmvXrsWYMWNw+vRp9OjRAwAwc+ZMvP322/rnODjwKrV07y5mFmP6muPILlbDzckWX07pi17+LqYui4jMgFFBNnr0aIP7//jHP7By5UocOXJEH2QODg7w8vJqvgqp3TuYlIuYdaegLNci2N0Ra6b1g78r/0EioipNPjqu1WqxceNGKJVKRERE6KevW7cObm5uCA0NxaJFi6BSqZqlUGqf1h9Nw4y1J6As12JAZ1dsnf0wQ4yIDBjd2eP8+fOIiIiAWq2Gk5MTtm3bhvvvvx8A8OyzzyIwMBA+Pj44d+4cFi5ciKSkJGzdurXO+Wk0Gmg0f4zYWlxc3ISXQW2NTifwz9jLWBV/DQAwvrcv3o8Og601eyYSkSGJELWNC1638vJypKWloaioCFu2bMGXX36J+Ph4fZjdaf/+/Rg6dCiuXr2K4ODgWuf31ltvYcmSJTWmFxUVQS7n6AztkbpCi1e+O4ufzmcBAOYO7YJ5kV3YqYOonSkuLoZCoWgwD4wOsrtFRkYiODgYq1atqvGYUqmEk5MTfv75ZwwfPrzW59e2Rebv788ga6eOXsvH4h8ScTm7BDZWEiwdH4YJffxMXRYRmUBjg+yezyPT6XQGQXSnM2fOAAC8vb3rfL5MJoNMJrvXMsjCZRep8d6uS/jhbNXlV1wcbLDiuQfwULCbiSsjInNnVJAtWrQIUVFRCAgIQElJCdavX4+DBw8iNjYWycnJWL9+PUaOHImOHTvi3LlzmD9/PgYNGoSwsLCWqp8snKZSi69+TcEn+69CVa6FRAJM6huA14Z3g6ujranLIyILYFSQ5ebmYvLkycjKyoJCoUBYWBhiY2Px+OOPIz09HXv37sXy5cuhVCrh7++P6OhovPHGGy1VO1m4A5dz8faPF5GSpwQAPBDggiVPhqKnn8LElRGRJbnnY2TNrbH7RMlypeYr8fbOi9h3ORcA4OYkw6KoEIzr7QuplB06iKhKqx0jI2osVXklVhxIxueHrqFcq4O1VIJpD3fCy0O7wNnOxtTlEZGFYpBRixNC4KfzWXjvp0vILFIDAAZ2ccPi0ffjPg9nE1dHRJaOQUYtKim7BG/9kIiEa/kAAL8O9nhj1P0Y3sOT54URUbNgkFGLKCqrwPK9v+N/CanQ6gRk1lLMHhKMFwcHw87GytTlEVEbwiCjZqXTCWw5mYF//nwZ+cpyAMCIHl7426juHCORiFoEg4yazZn0Qiz+IRFn0wsBAPd5OOGt0T3wSBee1ExELYdBRvdMCIHPD13D+z9fhhCAk8wa8yK7YMpDnWBjxUF+iahlMcjonmh1Am/9kIhvjqQCAMb19sWikSHwcLYzcWVE1F4wyKjJysq1eGnDaey9lAOJBHhj1P2Y8UiQqcsionaGQUZNkleqwYy1J3A2vRAyaymWT+yFqJ51Dw5NRNRSGGRktGs3SzF19XGk3VKhg4MNvpzyIPoEupq6LCJqpxhkZJQT12/hhf+dQKGqAgGuDlgzrS86uzuZuiwiascYZNRou89nYe6mMyiv1CHc3wVfTXkQbk68lhwRmRaDjBrly1+u4R+7LkEIILK7J/77TC842PLjQ0Smx28iqpdWJ/DuTxex+vB1AMDkiEAsHt0DVrzcChGZCQYZ1UldocXcjacRm5gDAHh9ZAhmDuzMwX6JyKwwyKhWt5TleGHtcZxKK4StlRT/ejoco8N9TF0WEVENDDKq4XqeElNXH8P1fBUU9jb4YvKD6BfE7vVEZJ4YZGTgdFoBZqw9gVvKcvi62GPt9L68+CURmTUGGenFJmZj7sbTUFfoEOorx9dT+3LMRCIyewwyAgCsOZyCJT9ehBDAo93c8cmzD8BRxo8HEZk/flO1czqdwNLdl/DFLykAgGf6BeCdMT1gzcuvEJGFYJC1Y1qdwILvzmDHmUwAwGvDu+EvQ4LZvZ6ILAqDrJ0SQuCdHy9ix5lM2FhJ8OGEcIzt7WvqsoiIjMYga6e++OUa1vx2HQDwr6d74UmeI0ZEFooHQtqhHWdu4L1dlwEAfxvZnSFGRBbNqCBbuXIlwsLCIJfLIZfLERERgd27d+sfV6vViImJQceOHeHk5ITo6Gjk5OQ0e9HUdIev5uHVzWcBANMfDsILA3lFZyKybEYFmZ+fH95//32cPHkSJ06cwGOPPYYxY8YgMTERADB//nzs3LkTmzdvRnx8PDIzMzF+/PgWKZyMdzGzGC9+cxIVWoFRYd54Y1R3duwgIosnEUKIe5mBq6srPvzwQ0yYMAHu7u5Yv349JkyYAAC4fPkyunfvjoSEBAwYMKBR8ysuLoZCoUBRURHkcvm9lEZ3yChQYfyK35BbokH/IFesnd4PdjZWpi6LiKhOjc2DJh8j02q12LhxI5RKJSIiInDy5ElUVFQgMjJS3yYkJAQBAQFISEho6mKoGRSqyjF19XHklmjQ1dMJn09+kCFGRG2G0b0Wz58/j4iICKjVajg5OWHbtm24//77cebMGdja2sLFxcWgvaenJ7Kzs+ucn0ajgUaj0d8vLi42tiSqh7pCi5n/O4GruaXwktthzbR+UNjbmLosIqJmY/QWWbdu3XDmzBkcPXoUs2fPxpQpU3Dx4sUmF7B06VIoFAr9zd/fv8nzIkNancD8TWdw/HoBnO2ssWZ6X/i42Ju6LCKiZmV0kNna2uK+++5Dnz59sHTpUoSHh+M///kPvLy8UF5ejsLCQoP2OTk58PLyqnN+ixYtQlFRkf6Wnp5u9IugmqpPeN59IRu2VlJ8/vyDCPHiMUcianvu+TwynU4HjUaDPn36wMbGBvv27dM/lpSUhLS0NERERNT5fJlMpu/OX32je/f5oTtPeA5HRHBH0xZERNRCjDpGtmjRIkRFRSEgIAAlJSVYv349Dh48iNjYWCgUCsyYMQMLFiyAq6sr5HI5XnrpJURERDS6xyI1j+2nb2Dp7qoTnt8Y1Z1XdiaiNs2oIMvNzcXkyZORlZUFhUKBsLAwxMbG4vHHHwcALFu2DFKpFNHR0dBoNBg+fDhWrFjRIoVT7Q5fzcNrW6pOeH7hkSC8MLCziSsiImpZ93weWXPjeWRNdzGzGE+vSkCpphJPhHnjv5N6QyrlCc9EZJla/DwyMi8ZBSpMXX0MpZpKDOjsin89Hc4QI6J2gUHWBhSqyjHl62PILdGgm6czVj3/IGTWPOGZiNoHBpmFU1do8cLaE0i+qYS3wg5rpvflCc9E1K4wyCyYVicwb+MZnEi9fcLztH7wVvCEZyJqXxhkFkoIgbd3JuLnxKoTnr+Y/CC6eTmbuiwiolbHILNQqw5dw9qEVEgkwL8nhmNAZ57wTETtE4PMAv18IQvv6094vh9PhPGEZyJqvxhkFia7SI2F358HAEx7uBNmPMIrPBNR+8YgsyBCCLy25SyKyioQ6ivHoqjupi6JiMjkGGQW5H8JqfjlSh5k1lIsn9gLttZ8+4iI+E1oIa7mluK9XZcAAIuiQnCfB3soEhEBDDKLUKHVYf6mM9BU6jCwixsmR3QydUlERGaDQWYB/rvvCs7fKILC3gYfTuAYikREd2KQmbmTqQX49MBVAMA/xoXCS2Fn4oqIiMwLg8yMKTWVWPDdGegEMK63L88XIyKqBYPMjL3700Wk5qvgo7DDW0/2MHU5RERmiUFmpvZezMGGY+mQSICPng7niPZERHVgkJmhvFIN/m/rOQDAC48E4aFgNxNXRERkvhhkZkYIgf/7/jzySsvRzdMZrwzrZuqSiIjMGoPMzHx3Ih17L+XA1kqKZRN7wc6GV3omIqoPg8yMpOYrsWTnRQDAK8O64n4fuYkrIiIyfwwyM1Gp1WHBd2ehKteiX5ArXhjY2dQlERFZBAaZmVh16BpOphbASWaNfz0VDiuO3kFE1CgMMjNw4UYRlsX9DgBY8mQP+Ls6mLgiIiLLwSAzMXWFFvM2nUGlTiAq1AvjH/A1dUlERBaFQWZi7+++jKu5pXB3luEf43pCIuEuRSIiYxgVZEuXLkXfvn3h7OwMDw8PjB07FklJSQZthgwZAolEYnB78cUXm7XotuKXKzex5rfrAIAPJ4TB1dHWtAUREVkgo4IsPj4eMTExOHLkCOLi4lBRUYFhw4ZBqVQatJs5cyaysrL0tw8++KBZi24LClXleHXzWQDA8wMCMaSbh4krIiKyTNbGNP75558N7q9ZswYeHh44efIkBg0apJ/u4OAALy+v5qmwDRJC4G/bLyCnWIPObo54fWR3U5dERGSx7ukYWVFREQDA1dXVYPq6devg5uaG0NBQLFq0CCqV6l4W0+b8cDYTP53LgpVUgmUTe8HelqN3EBE1lVFbZHfS6XSYN28eHn74YYSGhuqnP/vsswgMDISPjw/OnTuHhQsXIikpCVu3bq11PhqNBhqNRn+/uLi4qSVZhBuFZXhj+wUAwMuPdUG4v4tpCyIisnBNDrKYmBhcuHABv/76q8H0WbNm6X/v2bMnvL29MXToUCQnJyM4OLjGfJYuXYolS5Y0tQyLotMJvPrdWZSoK9HL3wUxj9ZcH0REZJwm7VqcM2cOfvzxRxw4cAB+fn71tu3fvz8A4OrVq7U+vmjRIhQVFelv6enpTSnJInx9OAUJ1/Jhb2OFZRN7wdqKZz8QEd0ro7bIhBB46aWXsG3bNhw8eBBBQUENPufMmTMAAG9v71ofl8lkkMlkxpRhkYpUFfhoT9WpCm880R1Bbo4mroiIqG0wKshiYmKwfv167NixA87OzsjOzgYAKBQK2NvbIzk5GevXr8fIkSPRsWNHnDt3DvPnz8egQYMQFhbWIi/AUnx/KgPqCh1CvJzxbL8AU5dDRNRmGBVkK1euBFB10vOdVq9ejalTp8LW1hZ79+7F8uXLoVQq4e/vj+joaLzxxhvNVrAlEkJg/bE0AMBz/QM4egcRUTMyetdiffz9/REfH39PBbVFx68X4GpuKextrDCmN8dSJCJqTuxt0Ao23N4aGx3uDbmdjYmrISJqWxhkLaxAWY6fzmcBAJ7tH2jiaoiI2h4GWQv7/lQGyit1uN9bjnA/hanLISJqcxhkLejOTh7PspMHEVGLYJC1oGMpt3DtphIOtlYY08vH1OUQEbVJDLIWVL019mS4D5zZyYOIqEUwyFrILWU5dp+vOmH82f48AZqIqKUwyFrI1lMZKNfqEOorR5ifi6nLISJqsxhkLeDOTh7PcDgqIqIWxSBrAUeuVXXycLS1wpheHMmDiKglMchagL6TRy9fOMmafMk3IiJqBAZZM8sv1SD2QlUnj+fYyYOIqMUxyJrZ97c7efT0VSDUlyN5EBG1NAZZMxJCYMOxqitcs8s9EVHrYJA1o4TkfKTkVXXyeDKcI3kQEbUGBlkzqu7kMaa3LxzZyYOIqFUwyJpJXqkGsYm3R/LguWNERK2GQdZMtpzMQIVWINyPnTyIiFoTg6wZ6HRCfxVodvIgImpdDLJmkHAtH6n5KjjJrPFEGDt5EBG1JgZZM1h/tGprbGxvH3byICJqZQyye3Sz5M5OHoEmroaIqP1hkN2jzSfTUakT6OXvgvt95KYuh4io3WGQ3QOdTmBj9Uge7HJPRGQSDLJ7cDg5D2m3VHCWWeOJcG9Tl0NE1C4xyO5BdSePcQ/4wsGWnTyIiEzBqCBbunQp+vbtC2dnZ3h4eGDs2LFISkoyaKNWqxETE4OOHTvCyckJ0dHRyMnJadaizUFuiRpxF6teF88dIyIyHaOCLD4+HjExMThy5Aji4uJQUVGBYcOGQalU6tvMnz8fO3fuxObNmxEfH4/MzEyMHz++2Qs3tc0nMlCpE+gd4IIQL3byICIyFYkQQjT1yTdv3oSHhwfi4+MxaNAgFBUVwd3dHevXr8eECRMAAJcvX0b37t2RkJCAAQMGNDjP4uJiKBQKFBUVQS43z4DQ6QQGf3QA6bfK8OGEMDz1oL+pSyIianMamwf3dIysqKgIAODq6goAOHnyJCoqKhAZGalvExISgoCAACQkJNzLoszKL1fzkH6rDM52HMmDiMjUmtxDQafTYd68eXj44YcRGhoKAMjOzoatrS1cXFwM2np6eiI7O7vW+Wg0Gmg0Gv394uLippbUajbc7uQxvrcv7G2tTFwNEVH71uQtspiYGFy4cAEbN268pwKWLl0KhUKhv/n7m/duutxiNeIuVXfy4EgeRESm1qQgmzNnDn788UccOHAAfn5++uleXl4oLy9HYWGhQfucnBx4eXnVOq9FixahqKhIf0tPT29KSa3muxPp0OoE+gR2QDcvZ1OXQ0TU7hkVZEIIzJkzB9u2bcP+/fsRFBRk8HifPn1gY2ODffv26aclJSUhLS0NERERtc5TJpNBLpcb3MyVViewgSN5EBGZFaOOkcXExGD9+vXYsWMHnJ2d9ce9FAoF7O3toVAoMGPGDCxYsACurq6Qy+V46aWXEBER0agei+bulys3caOwDHI7a4wK40geRETmwKggW7lyJQBgyJAhBtNXr16NqVOnAgCWLVsGqVSK6OhoaDQaDB8+HCtWrGiWYk2teiSP8Q/4wc6GnTyIiMzBPZ1H1hLM9TyynGI1Hnp/P7Q6gbj5g9DFk8fHiIhaUqucR9aebDpe1cmjb6cODDEiIjPCIGsErU5g0/GqTh7PsJMHEZFZYZA1wqHfqzp5KOxtMLInO3kQEZkTBlkjrLvdySOanTyIiMwOg6wBWUVl2H+5eiQP8x51hIioPWKQNWDDsXToBNCvkyvu82AnDyIic8Mgq4e6Qotvj6QCAJ6P4LiKRETmiEFWj+9PZeCWshy+LvaICq19rEgiIjItBlkddDqBL39JAQDMeCQI1lZcVURE5ojfznXYeykHKXlKyO2s8XRfdvIgIjJXDLI6fPHLNQBV1xxzkjX5+qNERNTCGGS1OJ1WgOPXC2BjJcHUhzqZuhwiIqoHg6wW1cfGngz3hZfCzsTVEBFRfRhkd0nLV2H3hSwAwMxBQQ20JiIiU2OQ3eXrwynQCWBQV3eEeJnPZWSIiKh2DLI7FKrK9aPczxrY2cTVEBFRYzDI7rDuaBrKKrTo7i3Hw/d1NHU5RETUCAyy2zSVWqw+fB0AMGtQECQSiWkLIiKiRmGQ3bbjdCbySjXwktvhiTAfU5dDRESNxCBD1XBUn98+AXr6I51gw+GoiIgsBr+xAcT/fhNXc0vhJLPGpH4Bpi6HiIiMwCAD8Pmhqq2xZ/r5Q25nY+JqiIjIGO0+yC7cKELCtXxYSSWY+jBPgCYisjTtPsiqBwd+Iswbvi72Jq6GiIiM1a6D7EZhGX48d3s4Kp4ATURkkdp1kK3+NQVancBDwR0R6qswdTlERNQERgfZoUOHMHr0aPj4+EAikWD79u0Gj0+dOhUSicTgNmLEiOaqt9kUlVVgw7E0AMDMQdwaIyKyVEYHmVKpRHh4OD799NM624wYMQJZWVn624YNG+6pyJaw8VgalOVadPFwwpCu7qYuh4iImsjoSx9HRUUhKiqq3jYymQxeXl5NLqqllVfq9MNRzRzUmcNRERFZsBY5Rnbw4EF4eHigW7dumD17NvLz81tiMU3247lMZBer4e4sw5heHI6KiMiSGb1F1pARI0Zg/PjxCAoKQnJyMl5//XVERUUhISEBVlZWNdprNBpoNBr9/eLi4uYuyYAQQn8C9NSHOkFmXbMmIiKyHM0eZJMmTdL/3rNnT4SFhSE4OBgHDx7E0KFDa7RfunQplixZ0txl1OnXq3m4nF0CB1srPNefw1EREVm6Fu9+37lzZ7i5ueHq1au1Pr5o0SIUFRXpb+np6S1aT/XW2NMP+sPFwbZFl0VERC2v2bfI7paRkYH8/Hx4e3vX+rhMJoNMJmvpMgAAl7KK8cuVPEglwIxHOBwVEVFbYHSQlZaWGmxdpaSk4MyZM3B1dYWrqyuWLFmC6OhoeHl5ITk5GX/9619x3333Yfjw4c1aeFN8+UsKACAq1Bv+rg4mroaIiJqD0UF24sQJPProo/r7CxYsAABMmTIFK1euxLlz57B27VoUFhbCx8cHw4YNwzvvvNNqW111yS5S44ezNwAALwzk1hgRUVthdJANGTIEQog6H4+Njb2nglrKmt+uo0Ir0K+TK3oHdDB1OURE1EzaxViLpZpKrDuaCoDDURERtTXtIsg2HU9HiboSnd0cMTTEw9TlEBFRM2rzQVap1eHrX6s6ebwwsDOkUg5HRUTUlrT5INt1IRs3CsvQ0dEW4x/wNXU5RETUzNp0kFUNR5UMAJgc0Ql2NhyOioiorWnTQXbk2i1cuFEMmbUUz0cEmrocIiJqAW06yL78pWo4qqce9IOrI4ejIiJqi9pskF3NLcG+y7mQSIAZj7DLPRFRW9Vmg6x6OKph93siyM3RxNUQEVFLaZNBlluixtZTVcNRzRzIrTEiorasxUe/NwWlRouH7uuI4rIK9AnkcFRERG1ZmwyyIDdHrJnWD+oKLSQSngBNRNSWtcldi9V43hgRUdvXpoOMiIjaPgYZERFZNAYZERFZNAYZERFZNAYZERFZNAYZERFZNLM7j0wIAQAoLi42cSVERGRK1TlQnQt1MbsgKykpAQD4+/ubuBIiIjIHJSUlUCgUdT4uEQ1FXSvT6XTIzMyEs7OzRY3KUVxcDH9/f6Snp0Mul5u6nEZj3a3PUmu31LoBy629vdcthEBJSQl8fHwgldZ9JMzstsikUin8/PxMXUaTyeVyi/rAVWPdrc9Sa7fUugHLrb09113fllg1dvYgIiKLxiAjIiKLxiBrJjKZDIsXL4ZMJjN1KUZh3a3PUmu31LoBy62ddTeO2XX2ICIiMga3yIiIyKIxyIiIyKIxyIiIyKIxyIiIyKIxyBph6dKl6Nu3L5ydneHh4YGxY8ciKSmp3uesWbMGEonE4GZnZ9dKFVd56623atQQEhJS73M2b96MkJAQ2NnZoWfPnti1a1crVWuoU6dONWqXSCSIiYmptb2p1vehQ4cwevRo+Pj4QCKRYPv27QaPCyHw5ptvwtvbG/b29oiMjMSVK1canO+nn36KTp06wc7ODv3798exY8dare6KigosXLgQPXv2hKOjI3x8fDB58mRkZmbWO8+mfN6au3YAmDp1ao06RowY0eB8TbnOAdT6eZdIJPjwww/rnGdrrPPGfP+p1WrExMSgY8eOcHJyQnR0NHJycuqdb1P/NmrDIGuE+Ph4xMTE4MiRI4iLi0NFRQWGDRsGpVJZ7/PkcjmysrL0t9TU1Faq+A89evQwqOHXX3+ts+1vv/2GZ555BjNmzMDp06cxduxYjB07FhcuXGjFiqscP37coO64uDgAwFNPPVXnc0yxvpVKJcLDw/Hpp5/W+vgHH3yA//73v/jss89w9OhRODo6Yvjw4VCr1XXOc9OmTViwYAEWL16MU6dOITw8HMOHD0dubm6r1K1SqXDq1Cn8/e9/x6lTp7B161YkJSXhySefbHC+xnzeWqL2aiNGjDCoY8OGDfXO09TrHIBBvVlZWfj6668hkUgQHR1d73xbep035vtv/vz52LlzJzZv3oz4+HhkZmZi/Pjx9c63KX8bdRJktNzcXAFAxMfH19lm9erVQqFQtF5RtVi8eLEIDw9vdPunn35ajBo1ymBa//79xZ///Odmrsx4c+fOFcHBwUKn09X6uDmsbwBi27Zt+vs6nU54eXmJDz/8UD+tsLBQyGQysWHDhjrn069fPxETE6O/r9VqhY+Pj1i6dGmr1F2bY8eOCQAiNTW1zjbGft6aQ221T5kyRYwZM8ao+ZjjOh8zZox47LHH6m1jinV+9/dfYWGhsLGxEZs3b9a3uXTpkgAgEhISap1HU/826sItsiYoKioCALi6utbbrrS0FIGBgfD398eYMWOQmJjYGuUZuHLlCnx8fNC5c2c899xzSEtLq7NtQkICIiMjDaYNHz4cCQkJLV1mvcrLy/Htt99i+vTp9Q4kbQ7r+04pKSnIzs42WKcKhQL9+/evc52Wl5fj5MmTBs+RSqWIjIw06ftQVFQEiUQCFxeXetsZ83lrSQcPHoSHhwe6deuG2bNnIz8/v8625rjOc3Jy8NNPP2HGjBkNtm3tdX7399/JkydRUVFhsP5CQkIQEBBQ5/pryt9GfRhkRtLpdJg3bx4efvhhhIaG1tmuW7du+Prrr7Fjxw58++230Ol0eOihh5CRkdFqtfbv3x9r1qzBzz//jJUrVyIlJQUDBw7UXyrnbtnZ2fD09DSY5unpiezs7NYot07bt29HYWEhpk6dWmcbc1jfd6teb8as07y8PGi1WrN6H9RqNRYuXIhnnnmm3gFgjf28tZQRI0bgf//7H/bt24d//vOfiI+PR1RUFLRaba3tzXGdr127Fs7Ozg3unmvtdV7b9192djZsbW1r/JNT3/pryt9Gfcxu9HtzFxMTgwsXLjS4HzoiIgIRERH6+w899BC6d++OVatW4Z133mnpMgEAUVFR+t/DwsLQv39/BAYG4rvvvmvUf3rm4quvvkJUVBR8fHzqbGMO67stqqiowNNPPw0hBFauXFlvW3P5vE2aNEn/e8+ePREWFobg4GAcPHgQQ4cObbU67sXXX3+N5557rsEOS629zhv7/dfauEVmhDlz5uDHH3/EgQMHjL7UjI2NDXr37o2rV6+2UHUNc3FxQdeuXeuswcvLq0ZPo5ycHHh5ebVGebVKTU3F3r178cILLxj1PHNY39XrzZh16ubmBisrK7N4H6pDLDU1FXFxcUZfjqOhz1tr6dy5M9zc3Oqsw5zWOQD88ssvSEpKMvozD7TsOq/r+8/Lywvl5eUoLCw0aF/f+mvK30Z9GGSNIITAnDlzsG3bNuzfvx9BQUFGz0Or1eL8+fPw9vZugQobp7S0FMnJyXXWEBERgX379hlMi4uLM9jSaW2rV6+Gh4cHRo0aZdTzzGF9BwUFwcvLy2CdFhcX4+jRo3WuU1tbW/Tp08fgOTqdDvv27WvV96E6xK5cuYK9e/eiY8eORs+joc9ba8nIyEB+fn6ddZjLOq/21VdfoU+fPggPDzf6uS2xzhv6/uvTpw9sbGwM1l9SUhLS0tLqXH9N+dtoqEhqwOzZs4VCoRAHDx4UWVlZ+ptKpdK3ef7558X//d//6e8vWbJExMbGiuTkZHHy5EkxadIkYWdnJxITE1ut7ldeeUUcPHhQpKSkiMOHD4vIyEjh5uYmcnNza6358OHDwtraWnz00Ufi0qVLYvHixcLGxkacP3++1Wq+k1arFQEBAWLhwoU1HjOX9V1SUiJOnz4tTp8+LQCIf//73+L06dP63n3vv/++cHFxETt27BDnzp0TY8aMEUFBQaKsrEw/j8cee0x8/PHH+vsbN24UMplMrFmzRly8eFHMmjVLuLi4iOzs7Fapu7y8XDz55JPCz89PnDlzxuAzr9Fo6qy7oc9ba9ReUlIiXn31VZGQkCBSUlLE3r17xQMPPCC6dOki1Gp1nbWbep1XKyoqEg4ODmLlypW1zsMU67wx338vvviiCAgIEPv37xcnTpwQERERIiIiwmA+3bp1E1u3btXfb8zfRmMxyBoBQK231atX69sMHjxYTJkyRX9/3rx5IiAgQNja2gpPT08xcuRIcerUqVate+LEicLb21vY2toKX19fMXHiRHH16tU6axZCiO+++0507dpV2Nraih49eoiffvqpVWu+U2xsrAAgkpKSajxmLuv7wIEDtX42qmvT6XTi73//u/D09BQymUwMHTq0xusJDAwUixcvNpj28ccf619Pv379xJEjR1qt7pSUlDo/8wcOHKiz7oY+b61Ru0qlEsOGDRPu7u7CxsZGBAYGipkzZ9YIJHNb59VWrVol7O3tRWFhYa3zMMU6b8z3X1lZmfjLX/4iOnToIBwcHMS4ceNEVlZWjfnc+ZzG/G00Fi/jQkREFo3HyIiIyKIxyIiIyKIxyIiIyKIxyIiIyKIxyIiIyKIxyIiIyKIxyIiIyKIxyIiIyKIxyIiIyKIxyIiIyKIxyIiIyKIxyIiIyKL9P02lQBKf/nMkAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "# 绘制曲线\n",
    "plt.figure(figsize=(5, 3))\n",
    "plt.plot(np.arange(1, max_epoch + 1), lossv)\n",
    "plt.title('validation loss')\n",
    "\n",
    "plt.figure(figsize=(5,3))\n",
    "plt.plot(np.arange(1, max_epoch + 1), top1AccuracyList)\n",
    "plt.title('validation top1 accuracy')\n",
    "\n",
    "plt.figure(figsize=(5,3))\n",
    "plt.plot(np.arange(1, max_epoch + 1), top5AccuracyList)\n",
    "plt.title('validation top5 accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b07deb8",
   "metadata": {
    "id": "fyWGvlbHup5w",
    "papermill": {
     "duration": 10.937489,
     "end_time": "2023-05-24T04:14:12.594348",
     "exception": false,
     "start_time": "2023-05-24T04:14:01.656859",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 4567.702953,
   "end_time": "2023-05-24T04:14:26.154490",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-05-24T02:58:18.451537",
   "version": "2.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
